A data governance and optimization system for progress visualization

Abstract

This invention discloses a data governance optimization system with visualized progress, comprising: a problem data status acquisition module for acquiring data record tags of problem data and the current problem data status of each unit; a data governance trend display module for generating a problem data rectification rate trend chart and a phased governance effect comparison chart based on the data governance progress trend analysis results; a governance status identification module for identifying the governance status of each unit and determining the reasons for the lag in lagging units; a lagging unit reminder module for generating lag reminder content for each lagging unit and sending it to the corresponding business domain leader and governance leader; and a rectification efficiency identification module for generating new lag reminder content for units that have not met the predetermined governance standards. This invention optimizes the display of data governance status, enabling management to fully grasp the governance progress of each unit, achieving real-time, dynamic, and intuitive information display, and significantly improving the efficiency and quality of data governance work.

Description

Technical Field

[0001] This invention relates to the field of data governance and information visualization, and in particular to a data governance optimization system for progress visualization. Background Technology

[0002] In the data governance process of ultra-high voltage transmission companies, the presentation of data governance status has always been a core issue requiring optimization. With increasing business complexity, the governance needs of various units in areas such as equipment status, fault records, and quality inspections have surged, leading to a gradual accumulation of data governance tasks. However, current data governance platforms suffer from information lag, lack of real-time updates, and inconspicuousness in displaying the governance progress of each unit. Due to the limitations of the platform in presenting data governance status, management finds it difficult to grasp the governance status and progress of each unit in a timely manner, especially regarding the governance effectiveness across different data types. This results in an inability to comprehensively assess governance progress and affects the rational allocation of governance resources. On the one hand, existing data governance platforms mostly provide only single indicators when displaying governance status, lacking dynamic governance progress charts and trend graphs. Managers can only see basic data such as the current number of tasks and the number of issues rectified, unable to intuitively understand the data governance trends of each unit through the data governance platform, nor can they compare governance effects at different stages, making it difficult to quantify the effectiveness of governance progress at different stages. Details in the governance process, such as the specific reasons for delays and the degree of data backlog, cannot be deeply analyzed using existing presentation methods, resulting in a lack of precise basis for management when formulating adjustment measures. Furthermore, data governance platforms fail to effectively display the governance status of upstream and downstream dependencies. Due to the interconnected nature of governance tasks, there are often dependencies between different units. However, existing platforms fail to visually present the lagging impact of upstream units to management, resulting in the failure to identify potential risks in downstream units in advance. Lagging upstream dependencies often cause governance delays in downstream units, but management cannot see these delays in the existing display, making it impossible to consider upstream and downstream coordination when allocating governance resources, further hindering overall governance progress. The current display mechanism also lacks dynamic feedback methods, making it difficult for management to assess whether lagging governance is being effectively followed up. Therefore, existing data governance platforms urgently need optimization in displaying data governance status, improving the dynamism, intuitiveness, and traceability of information so that management can comprehensively grasp the governance status, identify problems in real time, and adjust governance strategies promptly, thereby improving the overall effectiveness of data governance. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a data governance and optimization system for progress visualization, mainly comprising:

[0004] The Problem Data Status Acquisition Module is used to obtain the data record tags of problem data through the data governance platform, determine the classification attributes of problem data, and obtain the current problem data status of each unit.

[0005] The data governance trend display module is used to generate a view of the progress of problem data governance based on the status of problem data of each unit, and to generate a trend chart of problem data rectification rate and a comparison chart of stage governance effect based on the data governance progress trend analysis results;

[0006] The governance status identification module is used to acquire governance progress data of each unit, identify the governance status of each unit, and determine the reasons for the lag in lagging units.

[0007] The Delayed Unit Reminder Module is used to generate delay reminder content for each delayed unit based on the delay cause marker, delay quantity, and governance progress data, and send it to the corresponding business domain leader and governance leader.

[0008] The rectification efficiency identification module is used to update the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder, and to generate new delayed reminder content for units that have not met the predetermined governance standards.

[0009] Furthermore, the problem data status acquisition module is used to obtain the data record tags of the problem data through the data governance platform, determine the classification attributes of the problem data, and obtain the current problem data status of each unit, including:

[0010] Through the data governance platform of the ultra-high voltage transmission company, key information fields of the current problem data of each unit are obtained. Key information fields include data record tags, timestamps, and unit identifiers. Data record tags include, but are not limited to, monitoring equipment and line status monitoring systems. Based on the data record tags, the problem data is mapped to the corresponding data type to obtain the classification attribute of each problem data. The classification result of each problem data is stored in the additional fields of the data record. Data types include power line monitoring data, equipment status data, fault records, and quality inspection data. The classified problem data records are obtained, and different types of data are allocated to the corresponding partitioned databases for partitioned storage according to the classification attributes. Based on the partitioned storage data, the current problem data status of each unit is obtained, including the number of problem data, the number of data that has been rectified, and the number of new problems. The change in the number of data that has been rectified in the current period is compared with the previous data record to obtain the incremental information of the number of rectifications, and this information is updated in the data governance record table of each unit.

[0011] Furthermore, the data governance trend display module is used to generate a view of the progress of problem data governance based on the problem data status of each unit, and to generate a trend chart of problem data rectification rate and a comparison chart of stage governance effects based on the data governance progress trend analysis results, including:

[0012] Based on the data stored in partitions, the status of problem data for each unit is obtained, including the quantity of problem data, the quantity of data that has been rectified, and the quantity of new problems. Using a data visualization component, a progress view of problem data governance is generated based on the quantity of problem data, the quantity of rectified problem data, the quantity of problem data awaiting rectification, and the quantity of new problems for each unit. The progress view is updated in real time to monitor the data governance status of each unit. Based on the data governance record tables of each unit in the data governance platform and the current problem data status, the problem data status of each unit at different time points is obtained. The problem data rectification rate is calculated for each unit at each time point using the formula: Problem Data Rectification Rate = Quantity of Rectified Data / (Quantity of Rectified Data + Quantity of Problems Awaiting Rectification + Quantity of New Problems). This data is then arranged by timestamp to form a time-series problem data rectification rate dataset. A regression analysis algorithm is used to analyze the problem data rectification rate for each unit. Regression modeling is performed on time series data to fit a data governance progress trend line for each unit. Based on the slope of each trend line, the data rectification speed and progress direction of each unit are obtained. By calculating the average problem data rectification rate of each unit in different time periods, the data governance effect at different stages is judged. Using data visualization components, based on the data governance progress trend analysis results, a line chart is used to generate a problem data rectification rate trend chart to show the change trend of the problem data rectification rate of each unit, and a bar chart is used to generate a stage governance effect comparison chart to show the change of governance effect of each unit in different time periods. According to data visualization rules, a multi-dimensional chart report containing the problem data rectification rate trend chart and the stage governance effect comparison chart is generated to form a graphical display of governance progress and trends. Using an automatic report generation component, the governance progress, rectification rate trend and governance effect change of each unit are summarized in text, combined with chart display to generate a trend analysis report, and the report is uploaded to the data governance platform.

[0013] Furthermore, the governance status identification module is used to acquire governance progress data of each unit, identify the governance status of each unit, and determine the reasons for the lag in lagging units, including:

[0014] Through the data governance platform of the ultra-high voltage transmission company, real-time governance progress data of each unit is acquired, including the number of data items awaiting rectification, the number of data items that have been rectified, and the rectification rate of the data items. This data is recorded in time series and stored in a governance progress data table. The current rectification rate of each unit's data items is compared with a preset rectification rate threshold, and the number of data items awaiting rectification is compared with a preset quantity threshold. If the rectification rate is lower than the preset threshold or the number of data items awaiting rectification is higher than the preset quantity threshold, the unit's governance status is marked as lagging. Based on the growth rate of the backlog of data items in lagging units, it is determined whether it belongs to the category of data backlog-type lag. If the growth rate of the backlog exceeds a preset growth rate threshold, the cause of the lag is marked as a backlog of problematic data. Based on the rectification rate of problematic data at each time point for units with a lagging governance status, it is determined whether the rectification rate is low. If the calculated rectification rate of problematic data is consistently lower than the preset rectification rate threshold for several time periods, the cause of the lag is marked as a low rectification rate. If both a backlog of problematic data and a low rectification rate are met, the lag priority of the unit is marked as high-priority lag. Through the dependency chain lag analysis unit, the dependency chain lag risk of upstream dependent units and the cause of lag of downstream units are determined. The cause of lag, lag priority, and dependency chain lag risk of each unit are stored in the governance progress data table.

[0015] It also includes a dependency chain lag analysis unit, used to obtain dependency information for problem data governance tasks, determine the dependency chain lag risk of upstream dependent units and the lag causes of downstream units, specifically including:

[0016] Through the data governance platform of the ultra-high voltage transmission company, the dependency information of the current problem data governance task is obtained and stored in the governance progress data table. The dependency information includes the upstream dependent units of each unit's task, the data transmission timestamp, and the data type. Through the governance progress data table, the update status of the governance progress data of the upstream dependent units is monitored in real time. The number of data that has been rectified, the number of data that needs to be rectified, and the current problem data rectification rate of the upstream dependent units are obtained to determine whether the governance status of the upstream dependent units is lagging. If the governance status of the upstream dependent units is lagging, it is determined that the unit has a dependency chain lag risk, and the dependency chain lag risk is marked in the corresponding field of the governance progress data table. The governance progress data of the downstream units is obtained. If the problem data rectification rate of the downstream units is continuously lower than the expected rectification rate threshold after the data of the upstream dependent units lags, it is determined that the governance status of the downstream units is lagging because the upstream data lag is causing slow progress. The reason for the lag of the downstream units is marked as dependency lag, the lag priority of the upstream dependent units is marked as high priority lag, and the lag reason field and lag priority field in the governance progress data table are updated.

[0017] Furthermore, the lagging unit reminder module is used to generate a lagging reminder for each lagging unit based on the lagging cause marker, the number of lags, and the governance progress data, and send it to the corresponding business domain leader and governance leader, including:

[0018] The task reminder component of the data governance platform generates delayed reminder content for each lagging unit based on the delayed cause markers, delayed quantity, and governance progress data. It matches the data type of the problem data with the information of the business domain leader and governance leader to determine the recipient of each reminder, and appends the matched responsible person information to the reminder content. If the delayed unit's delay is due to a backlog of problem data or a low rectification rate, the delayed reminder content is sent via email. If the delayed unit has a high-priority delay, the delayed reminder content is sent via both SMS and email. A confirmation receipt is obtained, and based on successful or failed feedback, unsuccessfully sent reminder content is resent until it is successfully sent. The elevator attitude monitoring module updates the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder and generates new delayed reminder content for units that have not met the predetermined governance standards.

[0019] Furthermore, the rectification efficiency identification module is used to update the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder, and to generate new delayed reminder content for units that have not met the predetermined governance standards, including:

[0020] Acquire rectification status data for each unit after delayed reminders and update the data governance progress trend chart. Rectification status data includes the number of data that has been rectified, the number of data with pending issues, and the number of new issues. Based on the updated data governance progress trend chart, acquire real-time governance progress data for each unit, including the number of data that has been rectified, the number of data with pending issues, and the rectification rate of issue data. Based on preset rectification rate thresholds and preset quantity thresholds, determine whether the rectification progress of issue data has met the predetermined governance standards. If the predetermined governance standards have not been met, mark the rectification status of the unit as requiring a second reminder, determine the reason for the unit's delay, and generate new delayed reminder content. Send the new delayed reminder content to the business domain leader and governance leader through the delayed unit reminder module and record the sending status.

[0021] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0022] This invention provides a data governance optimization system with visualized progress. By automatically acquiring and classifying problem data, this invention generates a governance progress view, a problem data rectification rate trend chart, and a phased governance effect comparison chart, enabling management to intuitively understand the governance trends and effectiveness of each unit over different time periods. Simultaneously, this invention possesses functions for analyzing the causes of lag and managing dependency chain risks, identifying the specific reasons for lag in lagging units and the governance impact of upstream dependent units, allowing management to identify and resolve bottlenecks in data governance in advance. For lagging units, this invention generates targeted reminders and sends them to the responsible parties, forming a closed-loop reminder and feedback mechanism to ensure follow-up and implementation of rectification. The governance progress trend chart is updated based on the rectification status data after the lag reminder, achieving dynamic tracking of governance effects. By optimizing the display of data governance status, this invention enables management to comprehensively grasp the governance progress of each unit, achieving real-time, dynamic, and intuitive information display, helping management to quickly identify problems, adjust strategies, and significantly improve the efficiency and quality of data governance work. Attached Figure Description

[0023] Figure 1 This is a flowchart of a progress visualization data governance optimization system according to the present invention;

[0024] Figure 2 This is a schematic diagram of a progress visualization data governance and optimization system according to the present invention;

[0025] Figure 3 This is another schematic diagram of a progress visualization data governance and optimization system according to the present invention. Detailed Implementation

[0026] To make the objectives, technical strategies, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] like Figure 1-3 This embodiment of a progress visualization data governance optimization system may specifically include:

[0028] Step S101, Problem Data Status Acquisition Module, is used to obtain the data record tags of problem data through the data governance platform, determine the classification attributes of problem data, and obtain the current problem data status of each unit.

[0029] Through the data governance platform of the ultra-high voltage transmission company, key information fields of current problem data from various units are obtained. These key information fields include data record tags, timestamps, and unit identifiers. Data record tags include, but are not limited to, monitoring equipment and line status monitoring systems. Based on the data record tags, problem data is mapped to corresponding data types to obtain the classification attributes of each problem data entry. The classification results for each problem data entry are then stored in additional fields of the data record. Data types include power line monitoring data, equipment status data, fault records, and quality inspection data. The classified problem data records are then obtained, and different types of data are allocated to corresponding partitioned databases for partitioned storage according to their classification attributes. Based on the partitioned storage data, the current status of problem data for each unit is obtained, including the number of problem data entries, the number of data entries that have been rectified, and the number of new problems. The change in the number of data entries that have been rectified in the current period is compared with the previous data record to obtain the incremental information of the number of rectifications, which is then updated in the data governance record table of each unit.

[0030] For example, through the data governance platform of an ultra-high voltage transmission company, problem data from Unit A is obtained. Key information fields include a data record tag of "monitoring equipment," a timestamp of "2021-10-15 08:30:00," and a unit identifier of "Unit A." Based on the data record tag "monitoring equipment," the problem data is mapped to the power line monitoring data type, and this classification result is stored in an additional field of the data record. If Unit A's data contains 50 power line monitoring data entries, 30 equipment status data entries, 10 fault records, and 20 quality inspection data entries, after classification, these different types of data are allocated to partitioned databases for power line monitoring data, equipment status data, fault records, and quality inspection data, respectively. Based on the partitioned data, the current status of Unit A's problem data is obtained, where the total number of problem data entries is 110, the number of data entries that have been rectified is 40, and the number of newly added problem data entries is 15. If the number of data entries that were rectified in the previous cycle was 30, comparing the number of rectified data entries in the current cycle with the previous data record, the increase in the number of rectified data entries is 10. Update the incremental information to Unit A's data governance record table to track governance progress and support decision-making.

[0031] Step S102, the data governance trend display module, is used to generate a view of the progress of problem data governance based on the status of problem data of each unit, and to generate a trend chart of problem data rectification rate and a comparison chart of stage governance effect based on the data governance progress trend analysis results.

[0032] Based on the data stored in partitions, the status of problem data for each unit is obtained, including the quantity of problem data, the quantity of data that has been rectified, and the quantity of new problems. Using a data visualization component, a progress view of problem data governance is generated based on the quantity of problem data, the quantity of completed problem data, the quantity of problem data awaiting rectification, and the quantity of new problems for each unit. The progress view is updated in real time to monitor the data governance status of each unit. Based on the data governance record tables and current problem data status of each unit in the data governance platform, the problem data status at different time points for each unit is obtained. The problem data rectification rate is calculated for each unit at each time point using the formula: Problem Data Rectification Rate = Quantity of Rectified Data / (Quantity of Rectified Data + Quantity of Problems Awaiting Rectification + Quantity of New Problems). This data is then arranged by timestamp to form a time-series problem data rectification rate dataset. A regression analysis algorithm is used to model the time-series data of the problem data rectification rate for each unit, fitting a data governance progress trend line for each unit. Based on the slope of each trend line, the data rectification speed and progress direction of each unit are obtained. By calculating the average problem data rectification rate for each unit over different time periods, the data governance effectiveness at different stages is assessed. Using data visualization components, based on the data governance progress trend analysis results, a line chart is used to generate a trend chart of the problem data rectification rate, showing the changing trend of the problem data rectification rate for each unit. A bar chart is used to generate a comparison chart of the governance effects at different stages, showing the changes in the governance effects for each unit over different time periods. According to data visualization rules, a multi-dimensional chart report containing the problem data rectification rate trend chart and the comparison chart of the governance effects at different stages is generated, forming a graphical display of governance progress and trends. Using an automatic report generation component, a text summary is generated summarizing the governance progress, rectification rate trends, and changes in governance effects for each unit. This summary is combined with the charts to generate a trend analysis report, which is then uploaded to the data governance platform.

[0033] For example, by using partitioned storage, the status of problem data for Unit B is obtained. The total number of problem data entries is 120, with 50 completed and 20 new entries. Using a data visualization component, a progress view of Unit B's problem data governance is generated based on the 50 completed, 70 pending, and 20 new entries. A data change detection algorithm monitors Unit B's data governance status in real time. When the number of new entries increases from 20 to 25, a chart refresh is automatically triggered, updating the progress view. In the data governance platform, the status of problem data for Unit C at different time points is obtained. For example, at the beginning of January, Unit C had 40 completed, 60 pending, and 10 new entries. The problem data rectification rate for Unit C at the beginning of January is calculated using the formula: Problem Data Rectification Rate = Number of Completed Rectifications / (Number of Completed Rectifications + Number of Pending Rectifications + Number of New Entryes).

[0034] 40 / (40+60+10) = 0.4, or 40%. At the next time point, early February, the number of completed data items for Unit C was updated to 70, the number of issues awaiting rectification decreased to 40, and the number of new issues increased to 15. The rectification rate at the beginning of February was calculated to be 70 / (70+40+15) = 0.54, or 54%. These rectification rate calculations were arranged according to timestamps to form a time series dataset of Unit C's issue data rectification rate. Subsequently, a regression analysis algorithm was used to model the time series data of Unit C's rectification rate, fitting a trend line of Unit C's data governance progress. Based on the slope of the trend line, the data rectification speed of Unit C increased by approximately 0.14 per month, or 14%, showing a gradual improvement in Unit C's governance progress. By calculating the average issue data rectification rate for different time periods, it was determined that Unit C's average rectification rate in January was 40%, while the average rectification rate in February increased to 54%, indicating a significant improvement in data governance effectiveness in February. Based on data visualization components, a trend chart of the rectification rate for Unit C's issues was generated. A line chart showed the increase in the rectification rate from early January to early February. Simultaneously, a comparison chart of governance effectiveness at each stage was generated, using a bar chart to compare the average rectification rate in January and February, demonstrating the changes in governance effectiveness at each stage. Through an automatic report generation component, the governance progress, rectification rate trend, and changes in governance effectiveness for Unit C were summarized in text. For example, Unit C's rectification rate increased from 40% in January to 54% in February, showing a clear trend of improved governance progress. This summary, combined with the trend chart and comparison chart, generated a complete trend analysis report, which was uploaded to the data governance platform for management review and evaluation.

[0035] Step S103, the governance status identification module is used to obtain governance progress data of each unit, identify the governance status of each unit, and determine the reasons for the lag of lagging units.

[0036] Through the data governance platform of the ultra-high voltage transmission company, real-time governance progress data of each unit is acquired, including the number of data items awaiting rectification, the number of data items that have been rectified, and the rectification rate of the data items. This data is recorded in a time series and stored in a governance progress data table. The current rectification rate of each unit's data items is compared with a preset rectification rate threshold, and the number of data items awaiting rectification is compared with a preset quantity threshold. If the rectification rate is lower than the preset threshold or the number of data items awaiting rectification is higher than the preset quantity threshold, the unit's governance status is marked as lagging. Based on the growth rate of the backlog of data items for lagging units, it is determined whether the lag is due to a backlog of data items. If the growth rate is greater than a preset growth rate threshold, the cause of the lag is marked as a backlog of data items. Based on the rectification rate of data items at each time point for units with a lagging governance status, it is determined whether the rectification rate is low. If the calculated rectification rate is consistently lower than the preset rectification rate threshold for several time periods, the cause of the lag is marked as a low rectification rate. If both backlog of data items and low rectification rate are met simultaneously, the unit's lag priority is marked as high-priority lag. The dependency chain lag analysis unit is used to determine the dependency chain lag risk of upstream dependent units and the lag causes of downstream units. The lag causes, lag priorities, and dependency chain lag risks of each unit are stored in the governance progress data table.

[0037] For example, the data governance platform of the ultra-high voltage transmission company obtains the governance progress data of Unit D in real time. The current number of issues to be rectified in Unit D is 80, and 40 have been rectified. The rectification rate is calculated as 40 / (40+80) = 0.33, or 33%. This data is recorded in the governance progress data table in time series. Comparing the current rectification rate of Unit D with the preset rectification rate threshold of 50%, it is found that 33% of the issues have a rectification rate lower than the preset threshold. Simultaneously, the number of issues to be rectified in Unit D (80) is higher than the preset threshold of 60. Therefore, the governance status of Unit D is marked as lagging, and the data progress status of Unit D is color-coded in the issue data rectification rate trend chart: green indicates smooth progress, and red indicates lagging progress. A rectification rate > 50% indicates smooth progress, and a rectification rate < 50% indicates slow progress. Based on the backlog of problem data in Unit D, the growth rate of the number of problem data was calculated. It was found that the number of problem data items awaiting rectification in Unit D increased by 10 items per week over the past three weeks, exceeding the preset growth rate threshold of 5 items per week. Therefore, the reason for the lag was marked as a backlog of problem data. The rectification rate of problem data in Unit D at different time points was used to determine if there was a problem of low rectification rate. If the rectification rates of problem data in Unit D over the past three time points (three weeks) were 30%, 32%, and 33% respectively, all lower than the preset rectification rate threshold of 50%, then the reason for the lag was further marked as a low rectification rate. Since Unit D meets both the criteria for backlog of problem data and low rectification rate, its lag priority was marked as high-priority lag. The dependency chain lag analysis unit was used to determine the dependency chain lag risk of upstream dependent units and the lag reasons of downstream units. The lag reasons, lag priorities, and dependency chain lag risks of each unit were stored in the governance progress data table for easy follow-up and reminders.

[0038] The dependency chain lag analysis unit is used to obtain dependency information for the problem data governance task, and to determine the dependency chain lag risk of upstream dependent units and the lag reasons of downstream units.

[0039] The data governance platform of the ultra-high voltage transmission company is used to obtain dependency information for current problem data governance tasks and store it in a governance progress data table. Dependency information includes the upstream dependent units for each unit's task, data transmission timestamps, and data types. The governance progress data table is used to monitor the update status of upstream dependent units' governance progress data in real time, obtaining the number of completed data, the number of data to be rectified, and the current problem data rectification rate for each upstream dependent unit, to determine if the governance status of the upstream dependent unit is lagging. If the governance status of an upstream dependent unit is lagging, it is determined that the unit has a dependency chain lag risk, and the dependency chain lag risk is marked in the corresponding field of the governance progress data table. Downstream unit governance progress data is obtained. If the downstream unit's problem data rectification rate continues to be lower than the expected rectification rate threshold after the upstream dependent unit's data lag, it is determined that the downstream unit's governance status is lagging because the upstream data lag is causing slow progress. The reason for the downstream unit's lag is marked as dependency lag, the lag priority of the upstream dependent unit is marked as high-priority lag, and the lag reason field and lag priority field in the governance progress data table are updated.

[0040] For example, on the data governance platform of the ultra-high voltage transmission company, the dependency information of the problem data governance task of Unit F is obtained, showing that its upstream dependent unit is Unit E, the last timestamp of data transmission is 2020-02-01 12:00:00, and the data type is power line monitoring data. This dependency information is stored in the governance progress data table. Through the governance progress data table, it is observed that Unit E has completed the rectification of 60 data entries and has 90 data entries pending rectification. The current problem data rectification rate is 40%, which is lower than the set rectification rate threshold of 50%. Since Unit E's rectification rate is consistently below 50%, its governance status is judged to be lagging. Therefore, it is judged that Unit E has a dependency chain lag risk, and this risk is updated in the governance progress data table. Simultaneously, the governance progress data of downstream unit F was obtained. Analysis revealed that since unit E's data lag, unit F's problem data rectification rate has remained around 35%, while the expected rectification rate threshold is 50%. Therefore, it was determined that unit F's slow progress was affected by unit E's data lag. The cause of unit F's lag was marked as dependent lag, and the priority of unit E's lag was marked as high-priority lag. This information was then updated in the lag cause and lag priority fields of the governance progress data table.

[0041] Step S104, the lagging unit reminder module, is used to generate lagging reminder content for each lagging unit based on the lagging reason mark, lagging quantity and governance progress data, and send it to the corresponding business domain leader and governance leader.

[0042] The task reminder component of the data governance platform generates reminder content for each lagging unit based on the lagging cause flags, the number of lagging items, and governance progress data. By matching the data type of the problem data with the information of the business domain leader and governance leader, the recipient of each reminder is determined, and the matched responsible person information is appended to the reminder content. If the lagging unit's reason for lagging is a backlog of problem data or a low rectification rate, the reminder content is sent via email. If the lagging unit's priority is high-priority lagging, the reminder content is sent via both SMS and email. A confirmation receipt is obtained, and based on the success or failure feedback, unsuccessfully sent reminder content is resent until it is successfully sent.

[0043] For example, the task reminder component of the data governance platform generated a delayed reminder for Unit G. The reason for Unit G's delay was marked as a low rectification rate, with 50 delayed issues, and the current governance progress data showed a rectification rate of only 40%. Based on the data type and device status of Unit G's issues, the business domain manager was identified as N, and the governance manager as M. The recipients of the reminder were determined to be N and M. The system automatically queried the responsible person data table to obtain the names, positions, and contact information of the relevant responsible persons and appended their information to the reminder content. Since the reason for Unit G's delay was a low rectification rate, the reminder was sent via email. The email reminder included the reason for Unit G's delay (low rectification rate), the number of delayed issues (50), and the current rectification rate of the issue data (40%), and was sent to the email addresses of M and N. In another example, Unit H's delay priority was high-priority, caused by a backlog of problematic data and a persistently low rectification rate. A delay reminder was sent to the responsible person in Unit H via both SMS and email. The SMS message included information such as the number of delayed issues (80), the growth rate of the backlog (15 new issues per week), and the current rectification rate of problematic data (35%). A successful delivery confirmation was received and recorded in the delivery log for future tracking.

[0044] Step S105, the rectification efficiency identification module, is used to update the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder, and generate new delayed reminder content for units that have not met the predetermined governance standards.

[0045] Acquire rectification status data for each unit after delayed reminders and update the data governance progress trend chart. Rectification status data includes the number of data items that have been rectified, the number of data items awaiting rectification, and the number of newly added issues. Based on the updated data governance progress trend chart, acquire real-time governance progress data for each unit, including the number of data items that have been rectified, the number of data items awaiting rectification, and the rectification rate of issue data. Determine whether the rectification progress of issue data meets the predetermined governance standards based on preset rectification rate thresholds and preset quantity thresholds. If the predetermined governance standards are not met, mark the unit's rectification status as requiring a further reminder, determine the reason for the unit's delay, and generate new delayed reminder content. Send the new delayed reminder content to the business domain manager and governance manager through the delayed unit reminder module, and record the sending status.

[0046] For example, after a delayed reminder, the rectification status data of Unit J is obtained. The number of completed rectification data items is 60, the number of pending rectification issues is reduced to 40, and 10 new issues are added. Calculating the rectification increment of Unit J before and after the delayed reminder reveals that the number of completed rectification data items increased from 40 to 60, an increment of 20, indicating that the delayed reminder resulted in a rectification effect of 20 items. This increment is updated to the data governance progress trend chart to reflect the latest governance progress. Through the updated data governance progress trend chart, the current governance progress data of Unit J is obtained in real time. The data shows that the number of pending rectification issues is 40, the number of completed rectification issues is 60, and the rectification rate is 60 / (60+40+10) = 0.54, or 54%. Comparing the calculated rectification rate with the preset rectification rate threshold of 70%, and comparing the number of pending rectification issues (40) with the preset threshold of 30, it is found that the rectification rate reaches the preset threshold of 50%, but the number of rectified issues is higher than the preset value of 30. Therefore, the rectification status of Unit J is marked as requiring a reminder. The reason for the delay in Unit J is identified, and it is determined that the main problem is the backlog of problem data. Thus, a new delay reminder is generated, which includes Unit J's current rectification rate of 54%, the number of problems to be rectified of 40, and a detailed explanation of the reasons for the delay. This reminder is sent to Unit J's business domain manager K and governance manager L, and the sending status is recorded after the sending is completed so as to track the rectification effect in the future.

[0047] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A data governance and optimization system for progress visualization, characterized in that, The system includes: The Problem Data Status Acquisition Module is used to obtain the data record tags of problem data through the data governance platform, determine the classification attributes of problem data, and obtain the current problem data status of each unit. The data governance trend display module is used to generate a view of the progress of problem data governance based on the status of problem data of each unit, and to generate a trend chart of problem data rectification rate and a comparison chart of stage governance effect based on the data governance progress trend analysis results; The governance status identification module is used to acquire governance progress data of each unit, identify the governance status of each unit, and determine the reasons for the lag in lagging units. The Delayed Unit Reminder Module is used to generate delay reminder content for each delayed unit based on the delay cause marker, delay quantity, and governance progress data, and send it to the corresponding business domain leader and governance leader. The rectification efficiency identification module is used to update the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder, and generate new delayed reminder content for units that have not met the predetermined governance standards. The governance status identification module is used to acquire governance progress data of each unit, identify the governance status of each unit, and determine the reasons for the lag in lagging units, including: Through the data governance platform of the ultra-high voltage transmission company, real-time governance progress data of each unit is acquired, including the number of data items awaiting rectification, the number of data items that have been rectified, and the rectification rate of the data items. This data is recorded in time series and stored in a governance progress data table. The current rectification rate of each unit's data items is compared with a preset rectification rate threshold, and the number of data items awaiting rectification is compared with a preset quantity threshold. If the rectification rate is lower than the preset threshold or the number of data items awaiting rectification is higher than the preset quantity threshold, the unit's governance status is marked as lagging. Based on the growth rate of the backlog of data items in lagging units, it is determined whether it belongs to the category of data backlog-type lag. If the growth rate of the backlog exceeds a preset growth rate threshold, the cause of the lag is marked as a backlog of problematic data. Based on the rectification rate of problematic data at each time point for units with a lagging governance status, it is determined whether the rectification rate is low. If the calculated rectification rate of problematic data is consistently lower than the preset rectification rate threshold for several time periods, the cause of the lag is marked as a low rectification rate. If both a backlog of problematic data and a low rectification rate are met, the lag priority of the unit is marked as high-priority lag. Through the dependency chain lag analysis unit, the dependency chain lag risk of upstream dependent units and the cause of lag of downstream units are determined. The cause of lag, lag priority, and dependency chain lag risk of each unit are stored in the governance progress data table. The dependency chain lag analysis unit is used to obtain dependency information for the problem data governance task, determine the dependency chain lag risk of upstream dependent units and the lag causes of downstream units, including: Through the data governance platform of the ultra-high voltage transmission company, the dependency information of the current problem data governance task is obtained and stored in the governance progress data table. The dependency information includes the upstream dependent units of each unit's task, the data transmission timestamp, and the data type. Through the governance progress data table, the update status of the governance progress data of the upstream dependent units is monitored in real time. The number of data that has been rectified, the number of data that needs to be rectified, and the current problem data rectification rate of the upstream dependent units are obtained to determine whether the governance status of the upstream dependent units is lagging. If the governance status of the upstream dependent units is lagging, it is determined that the unit has a dependency chain lag risk, and the dependency chain lag risk is marked in the corresponding field of the governance progress data table. The governance progress data of the downstream units is obtained. If the problem data rectification rate of the downstream units is continuously lower than the expected rectification rate threshold after the data of the upstream dependent units lags, it is determined that the governance status of the downstream units is lagging because the upstream data lag is causing slow progress. The reason for the lag of the downstream units is marked as dependency lag, the lag priority of the upstream dependent units is marked as high priority lag, and the lag reason field and lag priority field in the governance progress data table are updated.

2. The system according to claim 1, wherein, The problem data status acquisition module is used to obtain data record tags for problem data through the data governance platform, determine the classification attributes of the problem data, and obtain the current problem data status of each unit, including: Through the data governance platform of the ultra-high voltage transmission company, key information fields of the current problem data of each unit are obtained. Key information fields include data record tags, timestamps, and unit identifiers. Data record tags include, but are not limited to, monitoring equipment and line status monitoring systems. Based on the data record tags, the problem data is mapped to the corresponding data type to obtain the classification attribute of each problem data. The classification result of each problem data is stored in the additional fields of the data record. Data types include power line monitoring data, equipment status data, fault records, and quality inspection data. The classified problem data records are obtained, and different types of data are allocated to the corresponding partitioned databases for partitioned storage according to the classification attributes. Based on the partitioned storage data, the current problem data status of each unit is obtained, including the number of problem data, the number of data that has been rectified, and the number of new problems. The change in the number of data that has been rectified in the current period is compared with the previous data record to obtain the incremental information of the number of rectifications, and this information is updated in the data governance record table of each unit.

3. The system according to claim 1, wherein, The data governance trend display module is used to generate a view of the progress of problem data governance based on the status of problem data of each unit, and to generate a trend chart of problem data rectification rate and a comparison chart of stage governance effects based on the data governance progress trend analysis results, including: Based on the data stored in partitions, the status of problem data for each unit is obtained, including the quantity of problem data, the quantity of data that has been rectified, and the quantity of new problems. Using a data visualization component, a progress view of problem data governance is generated based on the quantity of problem data, the quantity of rectified problem data, the quantity of problem data awaiting rectification, and the quantity of new problems for each unit. The data governance status of each unit is monitored in real time, and the progress view is updated if an update to the problem data status is detected. Based on the data governance record tables of each unit in the data governance platform and the current problem data status, the problem data status of each unit at different time points is obtained. The problem data rectification rate is calculated for each unit at each time point using the formula: Problem Data Rectification Rate = Quantity of Rectified Data / (Quantity of Rectified Data + Quantity of Problems Awaiting Rectification + Quantity of New Problems). This data is then arranged by timestamp to form a time-series problem data rectification rate dataset. A regression analysis algorithm is used to analyze the problem data rectification rate for each unit. Regression modeling is performed on time series data to fit a data governance progress trend line for each unit. Based on the slope of each trend line, the data rectification speed and progress direction of each unit are obtained. By calculating the average problem data rectification rate of each unit in different time periods, the data governance effect at different stages is judged. Using data visualization components, based on the data governance progress trend analysis results, a line chart is used to generate a problem data rectification rate trend chart to show the change trend of the problem data rectification rate of each unit, and a bar chart is used to generate a stage governance effect comparison chart to show the change of governance effect of each unit in different time periods. According to data visualization rules, a multi-dimensional chart report containing the problem data rectification rate trend chart and the stage governance effect comparison chart is generated to form a graphical display of governance progress and trends. Using an automatic report generation component, the governance progress, rectification rate trend and governance effect change of each unit are summarized in text, combined with chart display to generate a trend analysis report, and the report is uploaded to the data governance platform.

4. The system according to claim 1, wherein, The lagging unit reminder module is used to generate lagging reminder content for each lagging unit based on the lagging cause marker, lagging quantity, and governance progress data, and send it to the corresponding business domain leader and governance leader, including: The task reminder component of the data governance platform generates reminder content for each lagging unit based on the lagging cause markers, the number of lags, and governance progress data. By matching the data type of the problem data with the information of the business domain leader and the governance leader, the recipient of each reminder content is determined, and the matched responsible person information is attached to the reminder content. If the reason for the delay is a backlog of problematic data or a low rectification rate, a delay reminder will be sent via email. If the lagging unit has a high priority lag, the lag reminder will be sent via both SMS and email. Obtain a confirmation receipt. Based on the feedback of successful or failed delivery, resend the notification content that was not successfully sent until it is successfully sent. The elevator posture monitoring module is used to update the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder, and to generate new delayed reminder content for units that have not met the predetermined governance standards.

5. The system according to claim 1, wherein, The rectification efficiency identification module is used to update the data governance progress trend chart based on the rectification status data of each unit after the delayed reminder, and to generate new delayed reminder content for units that have not met the predetermined governance standards, including: Acquire rectification status data for each unit after delayed reminders and update the data governance progress trend chart. Rectification status data includes the number of data that has been rectified, the number of data with issues pending rectification, and the number of new issues. Based on the updated data governance progress trend chart, acquire real-time governance progress data for each unit, including the number of data that has been rectified, the number of data with issues pending rectification, and the rectification rate of issue data. Based on preset rectification rate thresholds and preset quantity thresholds, determine whether the rectification progress of issue data has reached the predetermined governance standards. If the predetermined governance standards have not been reached, mark the rectification status of the unit as requiring a second reminder, determine the reason for the unit's delay, and generate new delayed reminder content. The delayed unit reminder module sends new delayed reminder content to the business domain leader and governance leader, and records the sending status.

Images