Automatic inspection robot for enterprise information system

Abstract

The invention relates to the field of information technology automation, in particular to an automatic inspection robot for an enterprise information system, which comprises a configuration module used for reading a configuration file and obtaining operation parameters of an inspection task, a browser type, a target system website and login credential information; the test script execution module is used for regularly scheduling inspection tasks, simulating interaction between users and webpages or applications, executing related test scripts of the inspection tasks, realizing automatic function and performance tests and sending page data collected in the tests to the data processing module; the data processing module is used for carrying out exception analysis on the collected data, respectively informing the test script execution module and the informing module aiming at the exception data, adopting an exception handling and retry mechanism, carrying out log recording and storing a test result and a log; the notification module is used for providing an alarm notification according to the abnormal data; and the report generation module is used for providing a report after the inspection is finished.

Description

Technical Field

[0001] The present invention relates to the field of information technology automation, and in particular to an automated inspection robot for enterprise information systems. Background Art

[0002] As the number of information technology applications in major group companies increases, the functional modules of various business systems are also growing rapidly. The workload, complexity, and personnel time cost of daily functional inspections of enterprise business systems (such as ERP, CRM, MDM, etc.) will gradually increase. For enterprise information systems, manual inspections are currently widely used to inspect business systems multiple times a day. However, due to the large scale of the system and the many application functions, the inspection work occupies a large amount of human resources and time resources, and each inspection is a large-scale repetitive labor. Manual inspections may cause misoperations due to factors such as decreased attention, resulting in deviations in inspection results. There is an urgent need for an automated inspection robot for enterprise information systems that can improve the efficiency, accuracy, and stability of system inspections. It can also combine historical data for fault warning. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the prior art and propose an automated inspection robot for enterprise information systems to achieve efficient and accurate inspection and fault warning.

[0004] In order to achieve the above-mentioned object, the present invention proposes an automated inspection robot for enterprise information systems, including: a configuration module, which is used to read a configuration file, obtain the operation parameters of the inspection task and the browser type, the target system website and login credential information;

[0005] The test script execution module is used to schedule inspection tasks regularly, simulate the interaction between users and web pages or applications, execute relevant test scripts of inspection tasks, realize automated function and performance testing, and send the page data collected in the test to the data processing module;

[0006] The data processing module is used to perform abnormal analysis on the collected data, notify the test script execution module and the notification module respectively for abnormal data, adopt the exception handling and retry mechanism, and is used for log recording, and is also used to store test results and logs;

[0007] A notification module, used to provide alarm notifications based on abnormal data; and

[0008] The report generation module is used to provide a report after the inspection is completed.

[0009] Preferably, the configuration module also supports dynamic adjustment of configuration when the robot is running according to different test requirements.

[0010] Preferably, the scheduling of the inspection tasks at regular intervals includes automatically triggering the inspection tasks according to a set schedule by a task scheduler, and the schedule includes any one of daily, weekly and monthly.

[0011] Preferably, the simulating the interaction between the user and the webpage or application includes:

[0012] According to the read configuration file, access the login page of the target system URL through the browser.get(url) method of the Selenium library;

[0013] Use Xpath to locate the username and password input boxes, and use the element.send_keys(content) method to enter the specified text content;

[0014] Locate the button through Xpath and use the element.click() method to simulate a user click operation;

[0015] According to the relevant test scripts of the inspection task, navigate between different pages. In each page, automatically enter the form, submit the form, and collect page data in real time and send it to the data processing module. The page data includes: the status of page elements, user input data, system performance data and page structure data.

[0016] Preferably, the state of the page element includes the visibility of the element on the page, whether it is enabled, and whether it can be successfully clicked;

[0017] The system performance data includes: page loading time, response time and data submission processing time;

[0018] The page structure data is the DOM structure of the page.

[0019] Preferably, the exception handling and retry mechanism includes:

[0020] If the status of the page element is not displayed as expected, it is determined that there is a problem with page loading or element rendering, the exception is recorded, and the judgment result is sent to the notification module;

[0021] Collect system performance data regularly, compare the set threshold range with the page loading time and response time, and record the anomaly when it is determined that there is a bottleneck or delay in system performance, and send the judgment result to the notification module;

[0022] Verify the integrity of user input data through database API or direct access to the database. If data is lost or deformed, record the anomaly and send the result to the notification module.

[0023] Use Selenium automation tools to monitor each step of page redirection. By detecting the HTTP status code and page loading results, determine whether the page link can be accessed normally and whether the page redirects as expected. When the page redirection fails or the link has an HTTP error code, record the error code and try to load the page again. After multiple attempts reach the number of failures, determine that the redirection is abnormal, record the abnormality and send the judgment result to the notification module.

[0024] The recording of the exception includes automatically capturing a screenshot of the current web page and outputting the web page source code that caused the error.

[0025] Preferably, the data processing module combines the abnormal patterns in the historical inspection data and dynamically adjusts the inspection script to prioritize the inspection of high-risk modules in the enterprise information system to be tested. When the performance data of the high-risk modules or the status of the page elements deviate from the normal range and conform to the identified abnormal patterns, it is determined that there may be potential faults, and the judgment results are recorded and sent to the notification module. The abnormal patterns include a sudden drop in system performance and a page loading failure.

[0026] Preferably, the data processing module performs a multi-dimensional comprehensive analysis on all collected data. When data in multiple dimensions are abnormal at the same time, it is determined that there may be a major system failure, which is sent to the notification module first.

[0027] Preferably, the alarm notification includes: sending inspection abnormality text messages and emails to relevant persons, and generating inspection abnormality work orders.

[0028] Preferably, the robot also includes an integration module for integrating third-party services including short message platforms, email systems and operation management platforms, and is also used to provide interface scheduling, including interfaces for interacting with other systems, and start / stop testing and obtain test results.

[0029] Compared with the prior art, the advantages of the present invention are:

[0030] The core innovation of the present invention is to design an automated inspection robot for enterprise-level application systems, which can perform intelligent inspection, data collection and anomaly detection in a complex environment with multiple systems, multiple pages and multiple modules, and realize efficient automatic alarm and fault location through autonomous analysis. Compared with traditional manual inspection or semi-automatic inspection tools, the present invention has significant technical advantages in page element status detection, automatic anomaly identification, intelligent scheduling of inspection tasks, etc. It greatly improves the efficiency, accuracy and stability of system inspection. In practical applications, it has achieved good technical effects and is suitable for promotion in various enterprise application systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the method of the present invention;

[0032] Figure 2 It is the system composition block diagram;

[0033] Figure 3 It is a system deployment diagram;

[0034] Figure 4 This is an example of an inspection exception SMS;

[0035] Figure 5 This is an example of an inspection exception work order;

[0036] Figure 6 It is to take a screenshot of the inspection abnormality and leave a trace;

[0037] Figure 7 This is an example of the verification effect. DETAILED DESCRIPTION

[0038] The automated inspection robot is designed to provide an efficient and intelligent solution for functional testing of web pages and mobile applications. The robot integrates a variety of advanced Python libraries to implement its core functions, including but not limited to Selenium, CnOcr, PIL, ddddocr, pyDes, logging.handlers, configparser, os, sqlite3, smtplib, and requests. Through the synergy of these tools, the robot can accurately simulate the operations of human users, perform complex page element detection and interactive operations, and perform efficient data processing and feedback. This automated inspection system not only improves detection efficiency, but also greatly reduces manual intervention, making the functional testing process of web pages and mobile applications more reliable and intelligent. Figure 1 Shown is a flow chart of the robot automated inspection method of the present invention.

[0039] 1. Intelligent page navigation algorithm

[0040] The automated inspection robot uses a self-developed intelligent page navigation algorithm to automatically jump and navigate between different system pages according to the preset inspection script. This algorithm mainly solves the navigation problem caused by the diverse page structure and complex interaction logic in a multi-page complex system.

[0041] Path planning and dynamic adjustment: The robot automatically plans the optimal page inspection path according to the set task requirements and adjusts the inspection order in real time to ensure that all pages and functional modules that need to be inspected are covered.

[0042] Intelligent identification of page status: Using machine learning algorithms to analyze and identify page structure, it can not only handle traditional fixed forms and buttons, but also deal with dynamically loaded content and complex interactive front-end pages.

[0043] Fault backtracking mechanism: When an exception occurs in page navigation (such as page jump failure or loading delay), the navigation algorithm automatically backtracks and attempts to navigate again to avoid inspection interruptions.

[0044] 2. Anomaly detection and intelligent early warning algorithm

[0045] The anomaly detection algorithm is one of the core innovations of the present invention. It mainly analyzes the data collected during the inspection process in real time, intelligently determines the system operation status, and issues early warnings for potential problems.

[0046] Multi-dimensional anomaly detection: The algorithm performs real-time monitoring from multiple dimensions such as page element status, response time, data transmission, etc. For example, a button cannot be clicked, a page does not load as expected, and the system response time exceeds the threshold will all be considered potential anomalies.

[0047] Dynamic baseline establishment: The system can establish dynamic baseline values ​​based on historical inspection data, and determine whether there are system anomalies based on the deviation of real-time data. This adaptive anomaly judgment mechanism can continuously optimize the detection effect and avoid false alarms.

[0048] Automatic classification and processing of exceptions: When an exception is detected, the system automatically classifies and processes it according to the severity of the problem. For example, minor exceptions will generate alarm records, while serious exceptions will trigger emails or SMS notifications to operation and maintenance personnel, and even automatically generate work orders for tracking and processing.

[0049] 3. Automated data collection and analysis algorithms

[0050] The invention is equipped with powerful data collection and analysis algorithms, which can accurately collect data on the system operation status during the inspection process, and perform multi-dimensional data analysis through big data analysis technology to ensure the healthy operation of the system.

[0051] Page structure analysis: By analyzing the changes in the DOM structure of the page, check whether the page meets expectations, especially perform special processing on page elements with dynamically generated content.

[0052] Form input and data transmission monitoring: This algorithm can detect the accuracy of user form input data and the integrity of data submitted from the front end to the back end database, ensuring that the data is not lost or deformed during transmission within the system.

[0053] System performance analysis: The system can monitor performance indicators such as page loading speed and response time. By comparing historical data, it can determine whether there is a bottleneck or decline in the performance of the current system, and issue timely warnings.

[0054] 4. Autonomous learning and optimization algorithms

[0055] The inspection robot adopts an autonomous learning algorithm, which can continuously optimize its inspection path, data collection strategy and anomaly detection model as the inspection task is executed.

[0056] Adaptive adjustment of machine learning models: The system continuously adjusts and optimizes inspection scripts and detection strategies by analyzing historical inspection data, gradually improving inspection efficiency and accuracy.

[0057] Abnormal pattern recognition: Through the abnormal patterns accumulated in historical data, the system can more efficiently identify similar problems and quickly provide solutions, thereby improving the intelligence of inspections.

[0058] 5. Automatic alarm and work order generation mechanism

[0059] In order to ensure that the inspection results can be fed back in time and closed-loop processed, the present invention also integrates automatic alarm and work order generation mechanisms.

[0060] Multi-channel alarm notification: When a system abnormality is found during the inspection process, the robot can send alarm information to relevant personnel through various means (such as email, SMS, instant messaging, etc.).

[0061] Automatically generate work orders: In the event of serious abnormalities, the system can automatically generate and submit fault work orders to ensure that problems are systematically tracked and quickly resolved, thereby improving operation and maintenance management efficiency.

[0062] The inspection robot of the present invention is intended to solve the following key problems:

[0063] Large inspection workload: The system is large in scale, and the inspection work is cumbersome and frequent.

[0064] · Human error: Human inspection may lead to error due to factors such as operator fatigue.

[0065] Low inspection efficiency: Manual operation cannot achieve fast and consistent multiple inspections.

[0066] The automated inspection robot mainly inspects the following contents:

[0067] ●System login status: Verify whether the login function of each application system is normal, including the input of user name and password and page jump.

[0068] Availability of system function modules: Check whether each module can be opened and used normally, such as form input, button click, etc.

[0069] ●Data transmission and storage: Verify whether the system's data transmission function is normal and whether the data input can be correctly submitted and stored in the back-end database.

[0070] ●User interface element status: Check the display and clickability status of page elements to ensure that the front-end interface works properly.

[0071] The main functions of the robot include:

[0072] Web page interaction and data collection: Using the Selenium library, the robot can automatically navigate web pages and interact with web page elements, such as clicking buttons and filling out forms, to achieve complex interactive operations.

[0073] Verification code recognition and processing: Using the CnOcr and ddddocr libraries, the robot can recognize and process various verification codes and support common verification codes encountered during inspections.

[0074] Secure encryption and decryption: Through the pyDes library, the robot can encrypt and decrypt sensitive data to ensure the security of data during the test process.

[0075] Logging and Monitoring: With logging.handlers, the robot generates detailed log records during test execution, which facilitates problem tracking and performance monitoring.

[0076] Configuration management: Using configparser, the robot can flexibly handle multiple configuration files, making the test environment and parameters easy to adjust.

[0077] File and database operations: Through the os and sqlite3 libraries, the robot can perform file system operations and lightweight database interactions to support the management and storage of test data.

[0078] Automatic email notification: Using the smtplib library, the robot can automatically send email notifications at key nodes or after the test is completed, and report the test results in a timely manner.

[0079] Network request processing: With the requests library, the robot can perform HTTP requests for testing Web APIs and network connections.

[0080] In addition, the robot is designed with ease of use and scalability in mind, which not only adapts to current testing needs, but also provides support for possible technological development and functional expansion in the future. Overall, the automated inspection robot is a powerful tool that can be widely used in the development and quality assurance process of Web applications, aiming to improve testing efficiency, reduce error rates, and ensure the quality and reliability of software products.

[0081] The present invention aims to solve the above problems existing in the prior art and provide an automated inspection robot to achieve efficient and accurate inspection and fault warning.

[0082] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0083] Example

[0084] like Figure 2 As shown, an embodiment of the present invention provides an automated inspection robot for an enterprise information system, comprising:

[0085] The configuration module is used to read the configuration file, obtain the running parameters of the inspection task and the browser type, the target system website and the login credential information; and support dynamic adjustment of the configuration when the robot is running according to different test requirements.

[0086] The test script execution module is used to schedule inspection tasks regularly, simulate the interaction between users and web pages or applications, execute relevant test scripts of inspection tasks, realize automated function and performance testing, and send the page data collected in the test to the data processing module;

[0087] The data processing module is used to perform abnormal analysis on the collected data, notify the test script execution module and the notification module respectively for abnormal data, adopt the exception handling and retry mechanism, and is used for log recording, and is also used to store test results and logs;

[0088] Notification module, used to provide alarm notifications based on abnormal data;

[0089] The report generation module is used to provide a report after the inspection is completed.

[0090] The integration module is used to integrate third-party services including short message platforms, email systems and operation management platforms. It is also used to provide interface scheduling, including interfaces for interacting with other systems, and start / stop testing and obtain test results.

[0091] like Figure 3 The system deployment diagram is shown in Figure 1.

[0092] Inspection route

[0093] The automated inspection robot implements automated inspection tasks through predefined inspection tasks and scripts. The following are the specific implementation steps:

[0094] 1. Task scheduling and triggering

[0095] 1) Scheduled execution: Through the task scheduler (Windows Task Scheduler), the robot can automatically trigger inspection tasks according to a preset schedule (such as daily, weekly, and monthly).

[0096] 2) Automatic execution: After the inspection task is triggered, the robot starts to execute predefined operation steps.

[0097] 2. Initialization and preparation

[0098] 1) Loading the configuration file: The robot first loads the configuration file to obtain information such as task parameters, browser type, target system URL, login credentials, etc.

[0099] 2) Initialize the browser: Initialize the corresponding browser driver (ChromeDriver, FirefoxDriver) according to the browser type in the configuration file.

[0100] 3. Login operation

[0101] 1) Access the login page: The robot accesses the login page of the target system through the browser.get(url) method of the Selenium library.

[0102] 2) Enter login information: The robot uses Xpath to locate the username and password input boxes and uses the element.send_keys(content) method to enter the specified text content.

[0103] 3) Click the login button: locate the button through Xpath and use the element.click() method to simulate the user's click operation.

[0104] 4) Check page elements: Locate page elements through Xpath and check the status and attributes of elements, such as element.is_displayed(), element.is_enabled(), etc.

[0105] 4. Inspection steps execution

[0106] 1) Page navigation: According to the predefined inspection script, the robot navigates between different pages. The operation steps of each page include accessing the URL, clicking the link, entering the form, submitting the form, etc.

[0107] 2) Data collection: When executing operation steps, the robot collects page data in real time, including the status of page elements, text content and form input.

[0108] 3) Anomaly detection: After each operation, the robot checks whether the operation results are as expected. If an anomaly is found, the anomaly information is recorded and an alarm is issued.

[0109] 5. Data Collection and Analysis

[0110] 1) Data collection: The robot collects system operation data and page information through fixed operation steps. This data includes the status of page elements, text content, form input status, etc.

[0111] 2) Data analysis: Analyze the collected data to determine whether there are any abnormalities in the system. If an error occurs during the execution of an operation step, or the status of the page element does not match the expectation, it is considered abnormal.

[0112] 6. Alarm notification

[0113] 1) Email notification: When an abnormal situation is detected, the robot sends email warning information to relevant personnel through the smtplib library.

[0114] 2) SMS notification: When a serious abnormality is detected, the robot sends an emergency warning message via SMS. Figure 4 The following is an example of an inspection exception SMS.

[0115] 3) Sending work orders: When a problem is detected, a work order is automatically generated and sent to ensure that the problem is systematically tracked and handled. Figure 5 The following is an example of an inspection exception work order.

[0116] 7. Log records and screenshots

[0117] 1) Logging: Use the logging library to record every operation and abnormal situation during the inspection process.

[0118] 2) Screenshot trace: When an abnormality is detected, use Selenium's screenshot method to automatically capture a screenshot of the current page and save it to a specified path. Figure 6 The following are local screenshots of inspection anomalies.

[0119] like Figure 7 Shown is an example of the verification effect.

[0120] The core invention of the present invention is to realize the inspection of each functional module of the application system through an automated script, replace the manual inspection, and greatly improve the inspection efficiency.

[0121] Data collection

[0122] During the inspection process, the robot will collect data on the system's operating status in real time, specifically collecting the following types of data:

[0123] Page element status data: including the visibility of elements on the page, whether they are enabled, and whether they can be successfully clicked.

[0124] User input data: Collect the input data from the user form and ensure that the data is submitted correctly.

[0125] System response time: Collect page loading and response time data to check whether there are performance bottlenecks in the system.

[0126] Page structure data: Collect the DOM structure of the page and check whether the page content meets expectations.

[0127] Data collection is mainly carried out through the Selenium library, using Xpath and other methods to locate page elements and obtain their attribute values ​​and status.

[0128] Data Analysis and Error Judgment

[0129] The automated inspection robot of the present invention is not only responsible for collecting multi-dimensional data during system operation, but also analyzes various types of collected data in real time through built-in data analysis and error judgment mechanisms to ensure the health of the system. Data analysis includes detection of multiple dimensions such as page element status, system performance, data transmission, etc., and determines whether the system has abnormalities or errors through specific algorithms and rules.

[0130] 1. Page element status analysis

[0131] Analyze content: The system collects the visibility and interactivity of page elements (such as whether a button is clickable or a text box is inputtable), and analyzes whether these page elements display and work as expected.

[0132] Error judgment: If a page element does not display as expected (for example, a button is invisible or disabled, a text box cannot be edited), it is determined that there is a problem with page loading or element rendering. At this time, the robot will record the exception and trigger an early warning mechanism.

[0133] ●Analysis method: Through the Selenium automation tool, the inspection robot checks the page DOM structure and element attributes in real time, and uses Xpath and other technologies to locate elements and obtain their status. The robot will determine whether the element is working properly based on predefined element behavior standards.

[0134] 2. System performance analysis

[0135] Analysis content: The main analysis content of system performance includes page loading time, response time, data submission processing time, etc. The robot will monitor these performance indicators and compare the results with the set performance thresholds.

[0136] Error judgment: If the page loading time or system response time exceeds the preset threshold (for example, the page loading time exceeds 3 seconds or the response delay exceeds 500ms), it is determined that there is a bottleneck or delay problem in the system performance. At this time, the robot will record the anomaly and generate a performance analysis report to notify relevant personnel to optimize.

[0137] ●Analysis method: By regularly collecting system performance data, the robot can automatically analyze page loading speed and response time according to the set threshold range, and dynamically adjust the analysis criteria based on historical data.

[0138] Data transmission and storage analysis

[0139] Analysis content: The system analyzes the data submitted by the user to confirm whether the data is correctly submitted, stored and processed in the backend. After submitting the data, the inspection robot will verify the records in the database and compare the data integrity to ensure that there is no loss or deformation.

[0140] Error judgment: If data submission failure or inconsistent stored data is detected (for example, the data after form submission is not found in the database, or the field value is wrong), it is determined that there is a problem with data transmission or storage. At this time, the robot will generate a detailed error report and issue an early warning.

[0141] ●Analysis method: The robot verifies data through the database API or directly accesses the database, collects and analyzes the integrity of the data submitted. By comparing user input and database records, the robot can identify any data loss or deformation.

[0142] 4. Page jump and link analysis

[0143] ●Analysis content: When the system performs a page jump, it analyzes whether the navigation between pages is performed as expected and checks whether the page links can be accessed normally.

[0144] Error judgment: If the page jump fails or the link has an HTTP error code such as 404, 500, etc., the robot will immediately record the error and try to load the page again. After multiple failures, the system determines that the page jump function is abnormal and triggers an error report.

[0145] Analysis method: The robot uses Selenium automation tools to monitor each step of the page jump, and determines whether the page jumps successfully by detecting the HTTP status code and page loading results.

[0146] 5. Abnormal pattern recognition and dynamic analysis

[0147] Analysis content: Based on historical inspection data, the system uses abnormal pattern recognition technology to dynamically analyze the system's operating status and predict potential failures. The inspection robot can identify common abnormal patterns, such as sudden drops in system performance and page loading failures, and proactively optimize inspection tasks.

[0148] Error judgment: If the system's performance or status data deviates from the normal range and conforms to the identified abnormal pattern, it is determined that the system may have a potential fault. The robot will issue an early warning before the fault occurs, prompting maintenance personnel to intervene.

[0149] ·Analysis method: By analyzing abnormal patterns in historical data, the robot can identify signs of failure and dynamically adjust the inspection script to prioritize high-risk modules. This method significantly improves the timeliness of problem discovery.

[0150] 6. Comprehensive data analysis and multi-dimensional judgment

[0151] ●Analysis content: The system will conduct a comprehensive multi-dimensional analysis of all collected data, including page element status, system performance, data transmission, page jump, etc. The accuracy of the analysis results is ensured by cross-validating data of different dimensions.

[0152] Error judgment: When data in multiple dimensions are abnormal at the same time (for example, page element display errors and data submission failures occur at the same time), the robot will automatically determine that the module may have a major system failure and generate a high-priority error report.

[0153] Analysis method: The system ensures the accuracy and timeliness of anomaly detection by comprehensively analyzing various types of data and combining multiple criteria such as threshold judgment, pattern recognition, and historical data.

[0154] Core Algorithm

[0155] ●Page navigation algorithm: Based on the predefined inspection script, the robot completes the navigation operations between pages step by step, simulating the normal user usage process.

[0156] ●Anomaly detection algorithm: Automatically identifies system anomalies and triggers an alarm mechanism by collecting and analyzing the status of page elements and system response time.

[0157] ●Automatic alarm algorithm: When the system detects an abnormality, it automatically notifies the operation and maintenance personnel via email, text message, etc., and automatically generates a work order.

[0158] Through these core algorithms, the present invention effectively solves the time-consuming and labor-intensive problems of enterprises in daily system inspections, and improves the inspection efficiency and accuracy of the system.

[0159] 1. Automation of inspection tasks: The robot can automatically perform preset inspection tasks without human intervention.

[0160] 2. Data collection and analysis: The inspection robot collects system data in real time through browser operations and analyzes and processes it, mainly including page element status, form input, and system response time, to determine whether there are any abnormalities in the system.

[0161] 3. Intelligent early warning: Analyze inspection data through defined algorithms, predict potential failures, and issue early warning information.

[0162] 4. SMS notification: After the inspection is completed, the robot will send an inspection report to the operation and maintenance personnel via SMS, recording the inspection status and data in detail.

[0163] 5. Efficient processing: The robot can handle a large number of inspection tasks, improve overall inspection efficiency, and reduce operation and maintenance costs.

[0164] In addition, the robot is designed with ease of use and scalability in mind, which not only adapts to current testing needs, but also provides support for possible technological development and functional expansion in the future. Overall, the automated inspection robot is a powerful tool that can be widely used in the development and quality assurance process of Web applications, aiming to improve testing efficiency, reduce error rates, and ensure the quality and reliability of software products.

[0165] Technical effect:

[0166] 1. Reduced failures: Through real-time monitoring and intelligent early warning, the system failure rate is significantly reduced.

[0167] 2. Improved efficiency: Inspection efficiency is increased by about 90%, reducing the time and manpower required for manual inspections.

[0168] 3. Safety assurance: It reduces the safety risks of manual inspections and improves the safety of the working environment.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. An automated inspection robot for an enterprise information system, characterized in that: include: Configuration module, used to read the configuration file, obtain the running parameters of the inspection task and the browser type, target system website and login credential information; The test script execution module is used to schedule inspection tasks regularly, simulate the interaction between users and web pages or applications, execute relevant test scripts of inspection tasks, realize automated function and performance testing, and send the page data collected in the test to the data processing module; The data processing module is used to perform abnormal analysis on the collected data, notify the test script execution module and the notification module respectively for abnormal data, adopt the exception handling and retry mechanism, and is used for log recording, and is also used to store test results and logs; Notification module, used to provide alarm notifications based on abnormal data; and The report generation module is used to provide a report after the inspection is completed.

2. The automated inspection robot for enterprise information systems according to claim 1, characterized in that: The configuration module also supports dynamic adjustment of configuration when the robot is running according to different test requirements.

3. The automated inspection robot for enterprise information systems according to claim 1, characterized in that: The scheduling of the inspection task at a regular interval includes automatically triggering the inspection task according to a set schedule by a task scheduler, and the schedule includes any one of daily, weekly and monthly.

4. The automated inspection robot for enterprise information systems according to claim 1, characterized in that: The simulation of the interaction between the user and the webpage or application includes: According to the read configuration file, access the login page of the target system URL through the browser.get(url) method of the Selenium library; Use Xpath to locate the username and password input boxes, and use the element.send_keys(content) method to enter the specified text content; Locate the button through Xpath and use the element.click() method to simulate a user click operation; According to the relevant test scripts of the inspection task, navigate between different pages. In each page, automatically enter the form, submit the form, and collect page data in real time and send it to the data processing module. The page data includes: the status of page elements, user input data, system performance data and page structure data.

5. The automated inspection robot for enterprise information systems according to claim 4, characterized in that: The status of the page element, including the visibility of the element on the page, whether it is enabled, and whether it can be successfully clicked; The system performance data includes: page loading time, response time and data submission processing time; The page structure data is the DOM structure of the page.

6. The automated inspection robot for enterprise information systems according to claim 1, characterized in that: The exception handling and retry mechanism described herein includes: If the status of the page element is not displayed as expected, it is determined that there is a problem with page loading or element rendering, the exception is recorded, and the judgment result is sent to the notification module; Collect system performance data regularly, compare the set threshold range with the page loading time and response time, and record the anomaly when it is determined that there is a bottleneck or delay in system performance, and send the judgment result to the notification module; Verify the integrity of user input data through database API or direct access to the database. If data is lost or deformed, record the anomaly and send the result to the notification module. Use Selenium automation tools to monitor each step of page redirection. By detecting the HTTP status code and page loading results, determine whether the page link can be accessed normally and whether the page redirects as expected. When the page redirection fails or the link has an HTTP error code, record the error code and try to load the page again. After multiple attempts reach the number of failures, determine that the redirection is abnormal, record the abnormality and send the judgment result to the notification module. The recording of the exception includes automatically capturing a screenshot of the current web page and outputting the web page source code that caused the error.

7. The automated inspection robot for enterprise information systems according to claim 6, characterized in that: The data processing module combines the abnormal patterns in the historical inspection data and dynamically adjusts the inspection script to prioritize the inspection of high-risk modules in the enterprise information system to be tested. When the performance data of the high-risk modules or the status of the page elements deviate from the normal range and conform to the identified abnormal patterns, it is determined that there may be potential faults, and the judgment results are recorded and sent to the notification module. The abnormal patterns include sudden drops in system performance and page loading failures.

8. The automated inspection robot for enterprise information systems according to claim 6, characterized in that: The data processing module performs a multi-dimensional comprehensive analysis on all collected data. When data from multiple dimensions are abnormal at the same time, it is determined that there may be a major system failure and is sent to the notification module first.

9. The automated inspection robot for enterprise information systems according to claim 1, characterized in that: The alarm notification includes: sending inspection abnormality SMS and email to relevant persons, and generating inspection abnormality work order.

10. The automated inspection robot for enterprise information systems according to claim 1, characterized in that: The robot also includes an integration module for integrating third-party services including short message platforms, email systems and operation management platforms, and is also used to provide interface scheduling, including interfaces for interacting with other systems, and start / stop testing and obtain test results.

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