An intelligent inspection and management method for government websites based on RPA

Abstract

The present invention provides an RPA-based intelligent inspection and management method for government websites, and uses RPA technology to realize automatic monitoring and management of websites. It simulates user behavior to record data such as login frequency, identifies high-frequency access services, regularly captures website data and cleans it, sets thresholds to detect anomalies, and dynamically adjusts server resources according to user behavior to ensure efficient operation. It generates visual inspection reports based on the collected data to assist in decision-making, integrates RPA with the OA system to realize process automation approval, enhances information retrieval efficiency, and allows users to flexibly configure inspection items and optimize resource allocation. The entire process covers multiple aspects such as user behavior analysis, anomaly detection and prediction, dynamic resource adjustment, automatic report generation, process automation and personalized configuration, and aims to improve the operation and maintenance efficiency and service quality of government websites.

Description

Technical Field

[0001] The present invention relates to the field of automatic inspection of portal websites, and in particular to an intelligent inspection and management method for government websites based on RPA. Background Art

[0002] The RPA-based intelligent inspection and management method for government websites is a new management method developed under the background of government digital transformation. With the development of information technology, e-government has become one of the important means to improve government work efficiency and enhance the quality of public services. However, the traditional government website management method usually relies on manual regular inspection and maintenance, which is not only inefficient, but also prone to omissions and cannot promptly discover and deal with problems on the website.

[0003] In this context, RPA technology has become an effective tool for optimizing the management of government websites due to its high efficiency and accuracy. RPA can simulate human behavior patterns and automatically complete repetitive tasks such as data entry and file transfer. It can also be used for automated inspections of websites. Through RPA technology, uninterrupted monitoring of government websites can be achieved, and various problems in the operation of the website can be discovered and resolved in a timely manner, thereby ensuring the stability of the website and user experience.

[0004] In addition, with the development of big data technology and cloud computing, the massive data generated by government websites can be effectively collected, cleaned, analyzed and utilized, providing a scientific basis for the management and optimization of the websites. For example, by analyzing user behavior data, we can better understand user needs and optimize service processes. By analyzing website performance data, we can provide early warning of potential failures and improve system reliability.

[0005] Therefore, the RPA-based intelligent inspection and management method of government websites is a major improvement over traditional manual operations. It not only improves the management efficiency of government websites, but also enhances their service capabilities. It is an important direction for the development of modern e-government. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent inspection and management method for government websites based on RPA.

[0007] The problem to be solved by the present invention is: to simulate user behavior through RPA tools to collect data, automatically monitor website performance, predict and handle potential problems, dynamically adjust server resources, automatically generate inspection reports, integrate OA system to optimize approval processes, and flexibly configure inspection tasks, so as to improve the service quality and operation and maintenance efficiency of government websites, ensure stable operation of the website, respond to user needs and system anomalies in a timely manner, and ultimately improve user experience and government service efficiency.

[0008] An RPA-based intelligent inspection and management method for government websites. The technical solutions adopted are as follows:

[0009] S1: Identify user needs through user behavior data including login frequency, page dwell time, and click-through rate, define frequently accessed service items, use RPA tools to simulate user operations, record user behavior data, and identify usage scenarios;

[0010] S2: Use RPA technology to regularly capture various website data, including the number of visits, response time, and interface functions, filter invalid and abnormal data, including timeout records and links that cannot be opened, perform data cleaning, exclude invalid data, and pass data verification;

[0011] S3: Automatically detects abnormal situations by setting parameters including response time threshold, access frequency threshold, and error rate threshold, predicts possible problem patterns based on historical data, analyzes data, and predicts potential problems;

[0012] S4: Dynamically adjust server resource configuration based on user operations, behavior data, and usage scenarios simulated by the RPA tool in S1, and use the automatic expansion function of cloud services to dynamically adjust resources based on real-time monitoring data;

[0013] S5: Generate inspection reports containing abnormal conditions and recommended measures based on the collected data and analysis results to help managers quickly understand the situation and make decisions. Use BI tools for data analysis and visualization, develop automated report generation functions, and display indicator trend charts through visualization tools.

[0014] S6: Integrate RPA with the OA system to realize the automated approval of government affairs processes, develop keyword extraction functions to help users quickly find the required information, and integrate keyword extraction functions into the RPA process;

[0015] S7: Set the RPA inspection cycle, find problems and generate reports, establish emergency response processes, and quickly notify relevant personnel when abnormal situations are detected. Once an abnormality is found, an alarm notification will be sent immediately.

[0016] S8: Allows users to flexibly configure RPA inspection items according to their own needs, including setting inspection frequency and depth, dynamically adjusting server resource configuration according to the actual load of the government website, and providing a configuration interface. Users can adjust inspection items through the interface to achieve resource configuration adjustment.

[0017] Furthermore, the RPA tool is used in S1 to simulate user operations, record user behavior data, and identify usage scenarios, including:

[0018] S11: Use RPA tools to simulate real user access behavior and record user login frequency, page dwell time, click-through rate, search keywords, and user feedback data. Login frequency refers to the number of times a user logs in per day, page dwell time refers to the time a user stays on each page, click-through rate refers to the number of times a user clicks on each functional module on the page, search keywords refer to the keywords entered by the user in the search box, and user feedback refers to the opinions, suggestions, and complaints submitted by the user.

[0019] S12: Record the order in which users visit the website, analyze the user's navigation path, regard continuous operations within a period of time as a session, analyze the behavioral characteristics in the session, and record all clicks and search events that occur on the website;

[0020] S13: Discover periodic patterns in user behavior, analyze the trend of user behavior changing with seasons, analyze the behavioral differences of users in different situations based on their geographic location and device type, cluster user behavior data, identify user groups with similar behavior patterns, and identify the correlation between different user behaviors.

[0021] Furthermore, the S3 automatically detects abnormal situations, analyzes the data, and predicts potential problems, including:

[0022] S31: Determine the response time threshold based on historical data, set the maximum number of visits per unit time, set the error rate threshold, and use RPA to monitor the website's response time, access frequency, and error rate in real time.

[0023] S32: Dynamically adjust the threshold of anomaly detection based on real-time data to adapt to different load conditions. When any indicator is detected to exceed the preset threshold, it is automatically marked as abnormal and the specific situation of the abnormality is recorded;

[0024] S33: Store all historical monitoring data, use Python to write scripts to call RPA tools, and use the moving average method to apply to real-time data streams to predict possible problem patterns in the future.

[0025] Furthermore, the step S4 dynamically adjusts resources according to real-time monitoring data, including:

[0026] S41: Based on the real user access behavior simulated by the RPA tool used in S11 and the periodic pattern in the user behavior analyzed in S13, the system load is monitored in real time through the RPA tool, including CPU utilization and memory utilization;

[0027] S42: Use the automatic expansion function of cloud services to dynamically adjust server resources based on real-time monitoring data, use load balancers to evenly distribute traffic, use cache technology to reduce database access pressure, and use microservice architecture to allocate resources to different service components on demand;

[0028] S43: Design a multi-level adjustment strategy based on resource usage. When an increase in system load is detected, the CPU utilization exceeds 70%, the memory utilization exceeds 70%, and the database query frequency exceeds 70% of the design capacity per second, first adjust the cache size and then increase the server instance. First increase the cache size to reduce the pressure on database queries. After adjustment, the system load is still high, the CPU utilization exceeds 80%, the memory utilization exceeds 80%, and the database query frequency exceeds 80% of the design capacity per second. Expand the number of server instances and disperse the load by adding server instances.

[0029] Furthermore, the S5 develops an automated report generation function, and displays indicator trend charts through visualization tools, including:

[0030] Record all detected anomalies in detail, including time, location, and scope of impact, show the trend changes of various indicators, show the correlation analysis results between different indicators, show the time series change trend of various indicators, and mark the time point when the anomaly occurs in the trend chart.

[0031] Furthermore, in S6, RPA is integrated with the OA system to realize the automated approval of government affairs processes, and the keyword extraction function is integrated into the RPA process, including:

[0032] S61: Develop the interface between RPA and OA system, enable RPA to access the data and services of OA system, design the automation process, and automatically extract the documents to be approved from OA system;

[0033] S62: Convert the documents to be approved into a readable document format, use NLP technology to extract keywords from the documents, locate the required information, develop a document parsing module, generate keywords based on the document content, and display the keywords on the user interface;

[0034] S63: Before the approval process begins, RPA first runs the keyword extraction function to generate keywords for each document to be approved. During the approval process, the keywords are displayed to the approver. After the approval is completed, RPA automatically archives the file based on the keywords.

[0035] S64: Integrate keyword search function on government websites to allow users to quickly find relevant documents by keyword, and provide notifications to users who are interested in this area through keyword matching when new information is disclosed.

[0036] Furthermore, S8 allows users to flexibly configure RPA inspection items according to their own needs. Users can adjust inspection items through the interface to achieve resource configuration adjustment, including:

[0037] S81: allows users to select the functional modules to be inspected, set the inspection frequency and depth, set a fixed inspection time interval, and trigger an immediate inspection task at any time. Shallow inspection only checks whether the page is available and whether the response time meets expectations. Deep inspection includes functional testing and database query performance.

[0038] S82: Displays past inspection records, including inspection time and results, and displays past resource configuration adjustment records, including adjustment time and conditions before and after the adjustment. Users can flexibly configure inspection items according to their needs. After the user configuration is changed, the configuration results are fed back in real time.

[0039] Beneficial effects of the present invention: By simulating user operations through RPA technology and automatically recording user behavior data, the time cost of manual operations is reduced and work efficiency is improved. By analyzing data such as user login frequency and page dwell time, user needs can be understood more accurately, thereby optimizing service items and improving user experience.

[0040] Automatically detect various indicators of the website, such as response time, number of visits, etc., and set threshold warning mechanisms to detect abnormal situations in a timely manner to avoid larger failures caused by accumulated problems. Dynamically adjust server resource configuration based on real-time monitoring data, effectively utilize the elastic expansion capabilities of cloud computing, and ensure that the website can still maintain good performance under high load conditions;

[0041] By generating inspection reports containing abnormal situations and recommended measures, managers can quickly understand the situation and make decisions. At the same time, the data analysis and visualization of BI tools make complex data easier to understand and analyze;

[0042] Integrate RPA with the OA system to realize automated approval of government affairs, reduce human intervention, improve approval efficiency, and help users find required information faster through keyword extraction function;

[0043] Establish an emergency response process to ensure that once an abnormal situation is detected, relevant personnel can be notified quickly and corresponding measures can be taken to reduce risks. Allow users to flexibly configure RPA inspection items according to their own needs, which is more suitable for the specific situations of different users and improves applicability and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of an intelligent inspection and management method for government websites based on RPA. DETAILED DESCRIPTION

[0045] The present invention is further clearly and completely described below, but the protection scope of the present invention is not limited thereto.

[0046] An RPA-based intelligent inspection and management method for government websites. The technical solutions adopted are as follows:

[0047] S1: Identify user needs through user behavior data including login frequency, page dwell time, and click-through rate, define frequently accessed service items, use RPA tools to simulate user operations, record user behavior data, and identify usage scenarios;

[0048] S2: Use RPA technology to regularly capture various website data, including the number of visits, response time, and interface functions, filter invalid and abnormal data, including timeout records and links that cannot be opened, perform data cleaning, exclude invalid data, and pass data verification;

[0049] S3: Automatically detects abnormal situations by setting parameters including response time threshold, access frequency threshold, and error rate threshold, predicts possible problem patterns based on historical data, analyzes data, and predicts potential problems;

[0050] S4: Dynamically adjust server resource configuration based on user operations, behavior data, and usage scenarios simulated by the RPA tool in S1, and use the automatic expansion function of cloud services to dynamically adjust resources based on real-time monitoring data;

[0051] S5: Generate inspection reports containing abnormal conditions and recommended measures based on the collected data and analysis results to help managers quickly understand the situation and make decisions. Use BI tools for data analysis and visualization, develop automated report generation functions, and display indicator trend charts through visualization tools.

[0052] S6: Integrate RPA with the OA system to realize the automated approval of government affairs processes, develop keyword extraction functions to help users quickly find the required information, and integrate keyword extraction functions into the RPA process;

[0053] S7: Set the RPA inspection cycle, find problems and generate reports, establish emergency response processes, and quickly notify relevant personnel when abnormal situations are detected. Once an abnormality is found, an alarm notification will be sent immediately.

[0054] S8: Allows users to flexibly configure RPA inspection items according to their own needs, including setting inspection frequency and depth, dynamically adjusting server resource configuration according to the actual load of the government website, and providing a configuration interface. Users can adjust inspection items through the interface to achieve resource configuration adjustment.

[0055] Furthermore, the RPA tool is used in S1 to simulate user operations, record user behavior data, and identify usage scenarios, including:

[0056] S11: Use RPA tools to simulate real user access behavior and record user login frequency, page dwell time, click-through rate, search keywords, and user feedback data. Login frequency refers to the number of times a user logs in per day, page dwell time refers to the time a user stays on each page, click-through rate refers to the number of times a user clicks on each functional module on the page, search keywords refer to the keywords entered by the user in the search box, and user feedback refers to the opinions, suggestions, and complaints submitted by the user.

[0057] S12: Record the order in which users visit the website, analyze the user's navigation path, regard continuous operations within a period of time as a session, analyze the behavioral characteristics in the session, and record all clicks and search events that occur on the website;

[0058] S13: Discover periodic patterns in user behavior, analyze the trend of user behavior changing with seasons, analyze the behavioral differences of users in different situations based on their geographic location and device type, cluster user behavior data, identify user groups with similar behavior patterns, and identify the correlation between different user behaviors.

[0059] Furthermore, the S3 automatically detects abnormal situations, analyzes the data, and predicts potential problems, including:

[0060] S31: Determine the response time threshold based on historical data, set the maximum number of visits per unit time, set the error rate threshold, and use RPA to monitor the website's response time, access frequency, and error rate in real time.

[0061] S32: Dynamically adjust the threshold of anomaly detection based on real-time data to adapt to different load conditions. When any indicator is detected to exceed the preset threshold, it is automatically marked as abnormal and the specific situation of the abnormality is recorded;

[0062] S33: Store all historical monitoring data, use Python to write scripts to call RPA tools, and use the moving average method to apply to real-time data streams to predict possible problem patterns in the future.

[0063] Furthermore, the step S4 dynamically adjusts resources according to real-time monitoring data, including:

[0064] S41: Based on the real user access behavior simulated by the RPA tool used in S11 and the periodic pattern in the user behavior analyzed in S13, the system load is monitored in real time through the RPA tool, including CPU utilization and memory utilization;

[0065] S42: Use the automatic expansion function of cloud services to dynamically adjust server resources based on real-time monitoring data, use load balancers to evenly distribute traffic, use cache technology to reduce database access pressure, and use microservice architecture to allocate resources to different service components on demand;

[0066] S43: Design a multi-level adjustment strategy based on resource usage. When an increase in system load is detected, the CPU utilization exceeds 70%, the memory utilization exceeds 70%, and the database query frequency exceeds 70% of the design capacity per second, first adjust the cache size and then increase the server instance. First increase the cache size to reduce the pressure on database queries. After adjustment, the system load is still high, the CPU utilization exceeds 80%, the memory utilization exceeds 80%, and the database query frequency exceeds 80% of the design capacity per second. Expand the number of server instances and disperse the load by adding server instances.

[0067] Furthermore, the S5 develops an automated report generation function, and displays indicator trend charts through visualization tools, including:

[0068] Record all detected anomalies in detail, including time, location, and scope of impact, show the trend changes of various indicators, show the correlation analysis results between different indicators, show the time series change trend of various indicators, and mark the time point when the anomaly occurs in the trend chart.

[0069] Furthermore, in S6, RPA is integrated with the OA system to realize the automated approval of government affairs processes, and the keyword extraction function is integrated into the RPA process, including:

[0070] S61: Develop the interface between RPA and OA system, enable RPA to access the data and services of OA system, design the automation process, and automatically extract the documents to be approved from OA system;

[0071] S62: Convert the documents to be approved into a readable document format, use NLP technology to extract keywords from the documents, locate the required information, develop a document parsing module, generate keywords based on the document content, and display the keywords on the user interface;

[0072] S63: Before the approval process begins, RPA first runs the keyword extraction function to generate keywords for each document to be approved. During the approval process, the keywords are displayed to the approver. After the approval is completed, RPA automatically archives the file based on the keywords.

[0073] S64: Integrate keyword search function on government websites to allow users to quickly find relevant documents by keyword, and provide notifications to users who are interested in this area through keyword matching when new information is disclosed.

[0074] Furthermore, S8 allows users to flexibly configure RPA inspection items according to their own needs. Users can adjust inspection items through the interface to achieve resource configuration adjustment, including:

[0075] S81: allows users to select the functional modules to be inspected, set the inspection frequency and depth, set a fixed inspection time interval, and trigger an immediate inspection task at any time. Shallow inspection only checks whether the page is available and whether the response time meets expectations. Deep inspection includes functional testing and database query performance.

[0076] S82: Displays past inspection records, including inspection time and results, and displays past resource configuration adjustment records, including adjustment time and conditions before and after the adjustment. Users can flexibly configure inspection items according to their needs. After the user configuration is changed, the configuration results are fed back in real time.

[0077] The present invention provides an RPA-based intelligent inspection and management method for government websites, and uses RPA technology to realize automatic monitoring and management of websites. It simulates user behavior to record data such as login frequency, identifies high-frequency access services, regularly captures website data and cleans it, sets thresholds to detect anomalies, and dynamically adjusts server resources according to user behavior to ensure efficient operation. It generates visual inspection reports based on the collected data to assist in decision-making, integrates RPA with the OA system to realize process automation approval, enhances information retrieval efficiency, and allows users to flexibly configure inspection items and optimize resource allocation. The entire process covers multiple aspects such as user behavior analysis, anomaly detection and prediction, dynamic resource adjustment, automatic report generation, process automation and personalized configuration, and aims to improve the operation and maintenance efficiency and service quality of government websites.

Claims

1. An RPA-based intelligent inspection and management method for government websites, characterized in that: include: S1: Identify user needs through user behavior data including login frequency, page dwell time, and click-through rate, define frequently accessed service items, use RPA tools to simulate user operations, record user behavior data, and identify usage scenarios; S2: Use RPA technology to regularly capture various website data, including the number of visits, response time, and interface functions, filter invalid and abnormal data, including timeout records and links that cannot be opened, perform data cleaning, exclude invalid data, and pass data verification; S3: Automatically detects abnormal situations by setting parameters including response time threshold, access frequency threshold, and error rate threshold, predicts possible problem patterns based on historical data, analyzes data, and predicts potential problems; S4: Dynamically adjust server resource configuration based on user operations, behavior data, and usage scenarios simulated by the RPA tool in S1, and use the automatic expansion function of cloud services to dynamically adjust resources based on real-time monitoring data; S5: Generate inspection reports containing abnormal conditions and recommended measures based on the collected data and analysis results, use BI tools for data analysis and visualization, develop automated report generation functions, and display indicator trend charts through visualization tools; S6: Integrate RPA with the OA system to realize the automated approval of government affairs processes, develop keyword extraction functions, and integrate keyword extraction functions into the RPA process; S7: Set the RPA inspection cycle, find problems and generate reports, establish emergency response processes, and quickly notify relevant personnel when abnormal situations are detected. Once an abnormality is found, an alarm notification will be sent immediately. S8: Allows users to flexibly configure RPA inspection items according to their own needs, including setting inspection frequency and depth, dynamically adjusting server resource configuration according to the actual load of the government website, and providing a configuration interface. Users can adjust inspection items through the interface to achieve resource configuration adjustment.

2. The RPA-based intelligent inspection and management method for government websites according to claim 1, characterized in that: In S1, RPA tools are used to simulate user operations, record user behavior data, and identify usage scenarios, including: S11: Use RPA tools to simulate real user access behavior and record user login frequency, page dwell time, click-through rate, search keywords, and user feedback data. Login frequency refers to the number of times a user logs in per day, page dwell time refers to the time a user stays on each page, click-through rate refers to the number of times a user clicks on each functional module on the page, search keywords refer to the keywords entered by the user in the search box, and user feedback refers to the opinions, suggestions, and complaints submitted by the user. S12: Record the order in which users visit the website, analyze the user's navigation path, regard continuous operations within a period of time as a session, analyze the behavioral characteristics in the session, and record all clicks and search events that occur on the website; S13: Discover periodic patterns in user behavior, analyze the trend of user behavior changing with seasons, analyze the behavioral differences of users in different situations based on their geographic location and device type, cluster user behavior data, identify user groups with similar behavior patterns, and identify the correlation between different user behaviors.

3. The RPA-based intelligent inspection and management method for government websites according to claim 1, characterized in that: The S3 automatically detects anomalies, analyzes data, and predicts potential problems, including: S31: Determine the response time threshold based on historical data, set the maximum number of visits per unit time, set the error rate threshold, and use RPA to monitor the website's response time, access frequency, and error rate in real time. S32: Dynamically adjust the threshold of anomaly detection based on real-time data to adapt to different load conditions. When any indicator is detected to exceed the preset threshold, it is automatically marked as abnormal and the specific situation of the abnormality is recorded; S33: Store all historical monitoring data, use Python to write scripts to call RPA tools, and use the moving average method to apply to real-time data streams to predict possible problem patterns in the future.

4. The RPA-based intelligent inspection and management method for government websites according to claim 2, characterized in that: The step S4 dynamically adjusts resources according to real-time monitoring data, including: S41: Based on the real user access behavior simulated by the RPA tool used in S11 and the periodic pattern in the user behavior analyzed in S13, the system load is monitored in real time through the RPA tool, including CPU utilization and memory utilization; S42: Use the automatic expansion function of cloud services to dynamically adjust server resources based on real-time monitoring data, use load balancers to evenly distribute traffic, use cache technology to reduce database access pressure, and use microservice architecture to allocate resources to different service components on demand; S43: Design a multi-level adjustment strategy based on resource usage. When an increase in system load is detected, the CPU utilization exceeds 70%, the memory utilization exceeds 70%, and the database query frequency exceeds 70% of the design capacity per second, first adjust the cache size and then increase the server instance. First increase the cache size to reduce the pressure on database queries. After adjustment, the system load is still high, the CPU utilization exceeds 80%, the memory utilization exceeds 80%, and the database query frequency exceeds 80% of the design capacity per second. Expand the number of server instances and disperse the load by adding server instances.

5. The RPA-based intelligent inspection and management method for government websites according to claim 1, characterized in that: The automated report generation function is developed in S5, and the indicator trend chart is displayed through a visualization tool, including: Record all detected anomalies in detail, including time, location, and scope of impact, show the trend changes of various indicators, show the correlation analysis results between different indicators, show the time series change trend of various indicators, and mark the time point when the anomaly occurs in the trend chart.

6. The RPA-based intelligent inspection and management method for government websites according to claim 1, characterized in that: In the S6, RPA is integrated with the OA system to realize the automated approval of government affairs processes and integrate the keyword extraction function into the RPA process, including: S61: Develop the interface between RPA and OA system, enable RPA to access the data and services of OA system, design the automation process, and automatically extract the documents to be approved from OA system; S62: Convert the documents to be approved into a readable document format, use NLP technology to extract keywords from the documents, locate the required information, develop a document parsing module, generate keywords based on the document content, and display the keywords on the user interface; S63: Before the approval process begins, RPA first runs the keyword extraction function to generate keywords for each document to be approved. During the approval process, the keywords are displayed to the approver. After the approval is completed, RPA automatically archives the file based on the keywords. S64: Integrate keyword search functionality on government websites to allow users to quickly find relevant documents by keyword, and provide notifications to users through keyword matching when new information is made public.

7. The RPA-based intelligent inspection and management method for government websites according to claim 1, characterized in that: S8 allows users to flexibly configure RPA inspection items according to their own needs. Users can adjust inspection items through the interface to adjust resource configuration, including: S81: allows users to select the functional modules to be inspected, set the inspection frequency and depth, set a fixed inspection time interval, and trigger an immediate inspection task at any time. Shallow inspection only checks whether the page is available and whether the response time meets expectations. Deep inspection includes functional testing and database query performance. S82: Displays past inspection records, including inspection time and results, and displays past resource configuration adjustment records, including adjustment time and conditions before and after the adjustment. Users can flexibly configure inspection items according to their needs. After the user configuration is changed, the configuration results are fed back in real time.

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