Linux system-oriented training task automatic scoring system

Abstract

The invention relates to the technical field of teaching and practical training, and provides a Linux system-oriented practical training task automatic scoring system, which comprises a server side used for data storage and data hub and comprising a Redis service, a MySQL service and a task management engine; the teacher end is used for student management, task creation, check rule definition, task release, task modification and score viewing, and comprises a Streamline page service and an AI data analysis engine; the student end is used for task acquisition, task check rule code execution and task automatic scoring, a cross-platform client application program is constructed through Python, and the student end comprises a task synchronizer, a rule execution engine and a result reporting interface. According to the invention, the problems of low checking efficiency and many repeated operations due to dependence on manual judgment of teachers in operation system type practical training task checking in the traditional classroom teaching process are solved, a 7 * 24-hour real-time scoring function is provided, and the classroom teaching efficiency and the teaching effect are effectively improved.

Description

Technical Field

[0001] The embodiments of the present invention generally relate to the field of teaching and training technology, and in particular to an automatic scoring system, device and equipment for training tasks on a Linux system. Background Art

[0002] Driven by the rapid advancements in artificial intelligence, big data, and cloud computing, the demand for interdisciplinary talents in the computer science field, who possess both a solid theoretical foundation and exceptional engineering practical skills, continues to rise. Simultaneously, the booming development of information technology application innovation is accelerating the restructuring of the technological ecosystem, making mastery of Linux system operation and maintenance a core skill for practitioners in computer-related fields.

[0003] Cultivating Linux operating system skills requires not only solid theoretical knowledge but also extensive practical training to enhance students' problem-solving abilities. Currently, Linux system skill assessments rely primarily on manual review and grading by teachers. However, manual grading is inefficient, requiring teachers to spend considerable time individually verifying student performance, resulting in lengthy grading cycles and difficulty providing timely feedback. Manual grading is susceptible to subjective factors, and different teachers may have different criteria for the same operation, impacting the fairness and consistency of grading. Furthermore, manual methods cannot effectively monitor the entire student operation process, making it difficult to prevent cheating by tampering with the environment or reusing equipment.

[0004] Therefore, there is an urgent need to develop an automatic grading system specifically for Linux system training, which can realize dynamic verification of task execution, support automated inspection of complex operation processes, and have a built-in effective anti-cheating mechanism to ensure the fairness of the grading process. Such a system can significantly improve the efficiency and quality of practical teaching of Linux courses and provide strong support for cultivating high-quality operating system talents. Summary of the Invention

[0005] To solve the above problems, the present invention realizes automatic scoring of practical training tasks for Linux systems by executing predefined task checkpoint codes, which solves the problems that the inspection of operating system practical training tasks in traditional classroom teaching relies on teachers' manual judgment, has low inspection efficiency and many repetitive operations, and provides a 24 / 7 real-time scoring function, which effectively improves classroom teaching efficiency and teaching effectiveness.

[0006] According to an embodiment of the present invention, an automatic scoring system for practical training tasks in Linux systems is provided. The system includes:

[0007] Server side, teacher side, student side; among them,

[0008] The server is used for data storage and data hub, including: Redis service, MySQL service, and task management engine;

[0009] The teacher side is used for student management, task creation, inspection rule definition, task publishing, task modification, and grade review, including: Streamlit page service and AI data analysis engine;

[0010] The student side is used for task acquisition, task check rule code execution, and automatic task scoring. A cross-platform client application is built using Python, including: task synchronizer, rule execution engine, and result reporting interface.

[0011] Furthermore, the inspection rules include several inspection points, expected output of each inspection point, and score of each inspection point. The task management engine generates task inspection rule code based on the inspection rules.

[0012] Furthermore, the process of task release on the teacher side is as follows:

[0013] Step S10: Import student information individually or in batches through xls, xlsx, or csv files. The student information is written into MySQL.

[0014] Step S11: Specify the class to be taught, read student information from MySQL, and write the student class, student ID, and name information in a structured manner to the Redis service on the server side;

[0015] Step S12: Specify the course name, task name, and task description of the task, and set multiple checkpoints for the task. The checkpoints include the check type, parameter list, score, and error message. After the task is created, the task information is stored in MySQL in a structured form.

[0016] Step S13: Specify the course, task, and class information, generate the task check rule code from the structured task information in MySQL through the task check rule template, and write the course, class, task name, and task check rule code in a structured form into the Redis service on the server side.

[0017] Furthermore, the inspection types described in step S12 include:

[0018] File existence check: The parameters are file path and existence flag. If the file corresponding to the path exists and the existence flag is yes, or if the file corresponding to the path does not exist and the existence flag is no, the check is considered successful; otherwise, the check fails.

[0019] Instruction existence check: The parameters are instruction and existence flag. If the target device can execute the specified instruction and the existence flag is yes, or if the target device cannot execute the instruction and the existence flag is no, the check is considered successful. Otherwise, the check fails.

[0020] File content check: The parameters are the file path, the include flag, and the content to be matched. If the file corresponding to the path contains the specified content and the include flag is yes, or if the file corresponding to the path does not contain the specified content and the include flag is no, the check is considered successful; otherwise, the check fails.

[0021] Statement output check: The parameters are the statement to be executed, the include flag, and the expected output content. If the output result of the statement contains the expected content and the include flag is yes, or if the output result of the statement does not contain the expected content and the include flag is no, the check is considered successful; otherwise, the check fails.

[0022] Port openness check: The parameters are port number and open flag. If the designated port of the target device is open and the open flag is yes, or the designated port of the target device is not open and the open flag is no, the check is considered successful; otherwise, the check fails.

[0023] Furthermore, the process of automatic task scoring is as follows:

[0024] Step S20: The student opens the client application and enters the student information. The client application obtains the student information from the Redis service. If the information matches, the login is successful; otherwise, the login fails.

[0025] Step S21: The client application obtains the published tasks of the logged-in student's class from Redis, generates a task list and displays it on the page;

[0026] Step S22: The student configures the connection information of the device to be checked and tests the connection after completion. If the connection test is successful, the task can be checked and scored; otherwise, the task cannot be checked;

[0027] Step S23: The student selects a task to be graded through the client application. The client application obtains the task check rule code from the Redis service and executes the task check rule code through the rule execution engine to obtain the task score.

[0028] Step S24: The client application reports the student information, task information, and each task checkpoint check log in a structured form to the Redis service on the server side;

[0029] Step S25: After receiving the report information, the server parses the structured report data and writes it into the MySQL service.

[0030] Furthermore, the device to be checked in step S22 is a host in the student's local area network or a cloud host in the cloud that is equipped with a fingerprint mechanism. The fingerprint mechanism includes local inspection and server inspection. If the local inspection fails, the student is not allowed to perform the task inspection; if the server inspection fails, the student's score is invalid.

[0031] Furthermore, the local check operation process of the fingerprint mechanism is as follows:

[0032] Step S30: When students check their tasks, the student ID number, student name, MAC address of the device to be checked, and disk serial number are encrypted using SHA-256.

[0033] Step S31: Check whether there is a fingerprint file in the fingerprint directory. If so, go to step S32; otherwise, go to step S33;

[0034] Step S32: Compare the fingerprint file content with the generated ciphertext. If they are consistent, go to step S34; otherwise, go to step S35.

[0035] Step S33: Create a fingerprint file in the fingerprint directory, write the generated ciphertext, and go to step S34;

[0036] Step S34: The local check of the fingerprint passes;

[0037] Step S35: The local check of the fingerprint fails.

[0038] Furthermore, the fingerprint mechanism server-side check operation process includes:

[0039] Step S40: Create a fingerprint table on the server and save it in MySQL. The fingerprint table structure is: student ID, MAC address, disk serial number;

[0040] Step S41: When performing task inspection, obtain the MAC address and disk serial number of the host to be inspected;

[0041] Step S42: After the task check is completed, the host MAC address and disk serial number information are added to the reported data;

[0042] Step S43: After receiving the reported data, the server searches the fingerprint table for the student ID corresponding to the MAC address and disk serial number in the reported data;

[0043] Step S44: If the student ID number in the reported data is consistent with the queried student ID number, the fingerprint server check passes; otherwise, the fingerprint server check fails.

[0044] Furthermore, the process of the AI data analysis engine is as follows:

[0045] Step S50: In response to receiving the data analysis instruction, the teacher inputs the content to be analyzed in the form of natural language;

[0046] Step S51: In response to receiving the data analysis content input by the teacher, the AI data analysis engine reads the database table structure and generates SQL query statements and data visualization chart generation statements according to the teacher's request;

[0047] Step S52: in response to receiving the SQL query statement, executing the SQL query statement in the database to obtain the query result;

[0048] Step S53: in response to receiving the query result, executing a data visualization chart generation statement according to the query result to generate a visualization chart;

[0049] Step S54: In response to receiving the visualization chart generation, the query results and the visualization chart are displayed on the page.

[0050] Furthermore, the process of executing the task check rule by the rule execution engine is as follows:

[0051] Step S60: Initialize the counter score, initialize the result list msg, and enter all task check rules into the queue; if the queue is empty, directly output the result; otherwise, go to step S61;

[0052] Step S61: Pop the first checkpoint from the queue; execute the corresponding check code and record the output. If the output meets the expected result in the predefined parameter list, the counter score+score i , where score i If the score of the popped-up checkpoint is equal to the score of the popped-up checkpoint, go to step S62; otherwise, go to step S63;

[0053] Step S62: Check whether the queue is empty; if the queue is not empty, return to step S61 to process the next checkpoint; if the queue is empty, output the result;

[0054] Step S63: Add an element to the result list, whose value is the error message of the current checkpoint; go to step S62.

[0055] Beneficial effects of the present invention:

[0056] 1. Provide an automatic grading system. Teachers can use the system to issue tasks, check task completion status, and perform data analysis through natural language. Students can check their task completion status and receive task scores in real time. It provides multiple inspection rules to adapt to the needs of multiple scenarios and improve the efficiency of teaching feedback.

[0057] 2. Automatic scoring of practical training tasks for Linux systems is achieved by executing predefined task checkpoint codes, which solves the problem that the inspection of operating system practical training tasks in traditional classroom teaching relies on teachers' manual judgment, has low inspection efficiency and many repetitive operations. It provides a 24 / 7 real-time scoring function, effectively improving classroom teaching efficiency and teaching effectiveness.

[0058] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings, in which:

[0060] Figure 1 A diagram of an automatic scoring system for practical training tasks on a Linux system according to an embodiment of the present invention is shown;

[0061] Figure 2 It shows a time sequence diagram of a task release process on the teacher side of the automatic grading system for practical training tasks on a Linux system according to an embodiment of the present invention;

[0062] Figure 3 A flow chart of a student-side rule execution engine of an automatic grading system for practical training tasks on a Linux system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] According to the implementation mode of the present invention, an automatic scoring system for practical training tasks for Linux systems is proposed. By executing predefined task checkpoint codes, automatic scoring of practical training tasks for Linux systems is realized, which solves the problems of traditional classroom teaching in which the inspection of operating system practical training tasks relies on teachers' manual judgment, low inspection efficiency, and many repetitive operations. It provides a 24 / 7 real-time scoring function, which effectively improves classroom teaching efficiency and teaching effectiveness.

[0065] The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0066] Figure 1 This is a schematic diagram of an automatic scoring system for practical training tasks on a Linux system according to an embodiment of the present invention. The system includes:

[0067] Server side, teacher side, student side; among them,

[0068] The server is used for data storage and data hub, including: Redis service, MySQL service, and task management engine;

[0069] The teacher side is used for student management, task creation, inspection rule definition, task publishing, task modification, and grade review, including: Streamlit page service and AI data analysis engine;

[0070] The student side is used for task acquisition, task check rule code execution, and automatic task scoring. A cross-platform client application is built using Python, including: task synchronizer, rule execution engine, and result reporting interface.

[0071] It should be noted that although the operations of the system of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0072] In order to explain the above-mentioned automatic scoring system for practical training tasks for Linux systems more clearly, a specific embodiment is used for illustration below. However, it should be noted that this embodiment is only for better illustrating the present invention and does not constitute an improper limitation to the present invention.

[0073] The following is a more detailed explanation of the automatic scoring system for practical training tasks on Linux systems using a specific example:

[0074] like Figure 1 As shown, the automatic scoring method and system for practical training tasks for Linux systems include: a server side, a teacher side, and a student side.

[0075] The server provides data storage and data hub functions. The server consists of Redis service, MySQL service, and task management engine.

[0076] As a preferred embodiment of the present invention, the Redis service on the server side stores the student information of the currently available student terminal and the currently published task information; the MySQL service persistently stores the student information, task information, and scoring details; the teacher defines the inspection rules, which include multiple checkpoints, the expected output of each checkpoint, and the score of each checkpoint, and the task management engine generates the task inspection rule code through the inspection rules.

[0077] The teacher side provides student management, task creation, inspection rule definition, task publishing, task modification, and grade review. It consists of a Streamlit web page service and an AI data analysis engine. The teacher side provides web page services through Streamlit and natural language-based data analysis services through the AI data analysis engine. The teacher side page can be accessed directly using a browser.

[0078] As a preferred embodiment of the present invention, Figure 2 As shown in the figure, the process of task publishing on the teacher side is as follows:

[0079] Step S10: Add student information. You can import students individually or import multiple students in batches through xls, xlsx, or csv files. The student information is written to MySQL.

[0080] Step S11: Class opening, specify the class to be opened, read student information from MySQL, and write the student class, student number, and name information in a structured manner to the Redis service on the server. Specifically, the structure of the student information in the Redis service on the server is:

[0081]

[0082]

[0083] Step S12: Create a task, specify the course name, task name, and task description information, and set multiple checkpoints for the task. The checkpoints include the check type, parameter list, score, and error information. After the task is created, the task information is stored in MySQL in a structured form. Specifically, the task information stored in MySQL includes the task name, task description, and task checkpoint information. The structure of the task checkpoint information is as follows:

[0084]

[0085]

[0086] As a preferred embodiment of the present invention, the checkpoint prompt information is a prompt information given to students when the checkpoint is not passed, and information such as teaching material content guidance, error solution website, etc. can be added therein.

[0087] As a preferred embodiment of the present invention, before issuing a task, a task check rule code is generated based on task checkpoint information and a predefined template.

[0088] As a preferred embodiment of the present invention, the inspection types described in step S12 include:

[0089] File existence check: The parameters are the file path and the existence flag ([yes, no]); if the file corresponding to the path exists and the existence flag is "yes", or if the file does not exist and the existence flag is "no", the check is considered successful; otherwise, the check fails.

[0090] Instruction existence check: The parameters are the instruction and the existence flag ([yes, no]); if the specified instruction can be executed in the target device and the existence flag is "yes", or the instruction cannot be executed and the existence flag is "no", the check is considered successful; otherwise, the check fails.

[0091] File content check: The parameters are the file path, the include flag ([yes, no]), and the content to be matched. If the file corresponding to the path contains the specified content and the include flag is "yes", or does not contain the specified content and the include flag is "no", the check is considered successful; otherwise, the check fails.

[0092] Statement output check: The parameters are the statement to be executed, the inclusion flag ([yes, no]), and the expected output content. If the output result of executing the statement contains the expected content and the inclusion flag is "yes", or does not contain the expected content and the inclusion flag is "no", the check is considered successful; otherwise, the check fails.

[0093] Port openness check: The parameters are port number and open flag ([yes, no]); if the designated port of the target device is open and the open flag is "yes", or the port is not open and the open flag is "no", the check is considered successful; otherwise, the check fails.

[0094] As a preferred embodiment of the present invention, dynamic parameters can be added to the checkpoint, where the dynamic parameters ${student_id} represent the student ID, ${student_name} represents the student name, and ${class_name} represents the class name.

[0095] As a preferred embodiment of the present invention, the user can modify the task inspection rule template, add inspection types, and configure corresponding parameters and decision logic.

[0096] Step S13: Publish the task, specify the course, task, and class information, generate the task check rule code from the structured task information in MySQL through the task check rule template, and write the course, class, task name, and task check rule code in a structured form to the Redis service on the server. Specifically, the structure of the task information published in the Redis on the server is:

[0097]

[0098] As a preferred embodiment of the present invention, the process of the AI data analysis engine is as follows:

[0099] Step S50: In response to receiving the data analysis instruction, the teacher inputs the content to be analyzed in the form of natural language;

[0100] Step S51: In response to receiving the data analysis content input by the teacher, the AI data analysis engine reads the database table structure and generates SQL query statements and data visualization chart generation statements according to the teacher's request;

[0101] Step S52: in response to receiving the SQL query statement, executing the SQL query statement in the database to obtain the query result;

[0102] Step S53: in response to receiving the query result, executing a data visualization chart generation statement according to the query result to generate a visualization chart;

[0103] Step S54: In response to receiving the visualization chart generation, the query results and the visualization chart are displayed on the page.

[0104] The student side provides tasks acquisition, task check rule code execution, and automatic task grading. The student side uses Python to build a cross-platform client application, which consists of a task synchronizer, a rule execution engine, and a result reporting interface.

[0105] As a preferred embodiment of the present invention, Figure 3 As shown, the process of the rule execution engine executing task check rules is as follows:

[0106] Step S60: Initialize the counter score, initialize the result list msg, and enter all task check rules into the queue; if the queue is empty, directly output the result; otherwise, go to step S61;

[0107] Step S61: Pop the first checkpoint from the queue; execute the corresponding check code and record the output. If the output meets the expected result in the predefined parameter list, the counter score+score i , where score iIf the score of the popped-up checkpoint is equal to the score of the popped-up checkpoint, go to step S62; otherwise, go to step S63;

[0108] Step S62: Check whether the queue is empty; if the queue is not empty, return to step S61 to process the next checkpoint; if the queue is empty, output the result;

[0109] Step S63: Add an element to the result list, whose value is the error message of the current checkpoint; go to step S62.

[0110] As a preferred embodiment of the present invention, the process of automatic task scoring is as follows:

[0111] Step S20: The student opens the client application and enters the student information. The client application obtains the student information from the Redis service. If the information matches, the login is successful; otherwise, the login fails.

[0112] Step S21: The client application obtains the published tasks of the logged-in student's class from Redis, generates a task list and displays it on the page;

[0113] Step S22: The student configures the connection information for the device to be checked and tests the connection. If the connection test is successful, the task can be checked and graded; otherwise, the task cannot be checked. Specifically, the device to be checked is a host in the student's local area network or a cloud host in the cloud. A fingerprint mechanism is used to prevent multiple students from checking a single host. The fingerprint mechanism includes local and server-side checks. If the local check fails, the student is not allowed to check the task; if the server-side check fails, the student's score is invalid.

[0114] As a preferred embodiment of the present invention, the local check operation process of the fingerprint mechanism is as follows:

[0115] Step S30: When students check their tasks, the student ID number, student name, MAC address of the device to be checked, and disk serial number are encrypted using SHA-256.

[0116] Step S31: Check whether there is a fingerprint file in the fingerprint directory. If so, go to step S32; otherwise, go to step S33;

[0117] Step S32: Compare the fingerprint file content with the generated ciphertext. If they are consistent, go to step S34; otherwise, go to step S35.

[0118] Step S33: Create a fingerprint file in the fingerprint directory, write the generated ciphertext, and go to step S34;

[0119] Step S34: The local check of the fingerprint passes;

[0120] Step S35: The local check of the fingerprint fails.

[0121] As a preferred embodiment of the present invention, the fingerprint mechanism server-side check operation process includes:

[0122] Step S40: Create a fingerprint table on the server and save it in MySQL. The fingerprint table structure is: student ID, MAC address, disk serial number;

[0123] Step S41: When performing task inspection, obtain the MAC address and disk serial number of the host to be inspected;

[0124] Step S42: After the task check is completed, the host MAC address and disk serial number information are added to the reported data;

[0125] Step S43: After receiving the reported data, the server searches the fingerprint table for the student ID corresponding to the MAC address and disk serial number in the reported data;

[0126] Step S44: If the student ID number in the reported data is consistent with the queried student ID number, the fingerprint server check passes; otherwise, the fingerprint server check fails.

[0127] Step S23: The student selects a task to be graded through the client application. The client application obtains the task check rule code from the Redis service and executes the task check rule code through the rule execution engine to obtain the task score.

[0128] Step S24: The client application reports the student information, task information, and each task checkpoint check log in a structured form to the Redis service on the server side. Specifically, the task check information structure reported to the Redis on the server side is:

[0129]

[0130]

[0131] Step S25: After receiving the report information, the server parses the structured report data and writes it into the MySQL service.

[0132] Although the subject matter has been described in language specific to structural features and / or system logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. The automatic scoring system for practical training tasks on Linux system is characterized by: The system includes: Server side, teacher side, student side; among them, The server is used for data storage and data hub, including: Redis service, MySQL service, and task management engine; The teacher side is used for student management, task creation, inspection rule definition, task publishing, task modification, and grade review, including: Streamlit page service and AI data analysis engine; The student side is used for task acquisition, task check rule code execution, and automatic task scoring. A cross-platform client application is built using Python, including: task synchronizer, rule execution engine, and result reporting interface.

2. The automatic scoring system for practical training tasks for Linux systems according to claim 1 is characterized in that: The inspection rules include several inspection points, expected output of each inspection point, and score of each inspection point. The task management engine generates task inspection rule code based on the inspection rules.

3. The automatic scoring system for practical training tasks for Linux systems according to claim 1 is characterized in that: The process of releasing tasks on the teacher side is as follows: Step S10: Import student information individually or in batches through xls, xlsx, or csv files. The student information is written into MySQL. Step S11: Specify the class to be taught, read student information from MySQL, and write the student class, student ID, and name information in a structured manner to the Redis service on the server side; Step S12: Specify the course name, task name, and task description of the task, and set multiple checkpoints for the task. The checkpoints include the check type, parameter list, score, and error message. After the task is created, the task information is stored in MySQL in a structured form. Step S13: Specify the course, task, and class information, generate the task check rule code from the structured task information in MySQL through the task check rule template, and write the course, class, task name, and task check rule code in a structured form into the Redis service on the server side.

4. The automatic scoring system for practical training tasks for Linux systems according to claim 3 is characterized in that: The types of checks described in step S12 include: File existence check: The parameters are file path and existence flag. If the file corresponding to the path exists and the existence flag is yes, or if the file corresponding to the path does not exist and the existence flag is no, the check is considered successful; otherwise, the check fails. Instruction existence check: The parameters are instruction and existence flag. If the target device can execute the specified instruction and the existence flag is yes, or if the target device cannot execute the instruction and the existence flag is no, the check is considered successful. Otherwise, the check fails. File content check: The parameters are the file path, the include flag, and the content to be matched. If the file corresponding to the path contains the specified content and the include flag is yes, or if the file corresponding to the path does not contain the specified content and the include flag is no, the check is considered successful; otherwise, the check fails. Statement output check: The parameters are the statement to be executed, the include flag, and the expected output content. If the output result of the statement contains the expected content and the include flag is yes, or if the output result of the statement does not contain the expected content and the include flag is no, the check is considered successful; otherwise, the check fails. Port openness check: The parameters are port number and open flag. If the designated port of the target device is open and the open flag is yes, or the designated port of the target device is not open and the open flag is no, the check is considered successful; otherwise, the check fails.

5. The automatic scoring system for practical training tasks for Linux systems according to claim 1 is characterized in that: The process of automatic task scoring is as follows: Step S20: The student opens the client application and enters the student information. The client application obtains the student information from the Redis service. If the information matches, the login is successful; otherwise, the login fails. Step S21: The client application obtains the published tasks of the logged-in student's class from Redis, generates a task list and displays it on the page; Step S22: The student configures the connection information of the device to be checked and tests the connection after completion. If the connection test is successful, the task can be checked and scored; otherwise, the task cannot be checked; Step S23: The student selects a task to be graded through the client application. The client application obtains the task check rule code from the Redis service and executes the task check rule code through the rule execution engine to obtain the task score. Step S24: The client application reports the student information, task information, and each task checkpoint check log in a structured form to the Redis service on the server side; Step S25: After receiving the report information, the server parses the structured report data and writes it into the MySQL service.

6. The automatic scoring system for practical training tasks for Linux systems according to claim 5 is characterized in that: The device to be checked in step S22 is a host in the student's local area network or a cloud host in the cloud that is equipped with a fingerprint mechanism. The fingerprint mechanism includes local inspection and server inspection. If the local inspection fails, the student is not allowed to perform the task inspection; if the server inspection fails, the student's score is invalid.

7. The automatic scoring system for practical training tasks for Linux systems according to claim 6 is characterized in that: The local check operation process of the fingerprint mechanism is: Step S30: When students check their tasks, the student ID number, student name, MAC address of the device to be checked, and disk serial number are encrypted using SHA-256. Step S31: Check whether there is a fingerprint file in the fingerprint directory. If so, go to step S32; Otherwise go to step S33; Step S32: Compare the fingerprint file content with the generated ciphertext. If they are consistent, go to step S34; otherwise, go to step S35. Step S33: Create a fingerprint file in the fingerprint directory, write the generated ciphertext, and go to step S34; Step S34: The local check of the fingerprint passes; Step S35: The local check of the fingerprint fails.

8. The automatic scoring system for practical training tasks for Linux systems according to claim 6 is characterized in that: The fingerprint mechanism server-side check operation process includes: Step S40: Create a fingerprint table on the server and save it in MySQL. The fingerprint table structure is: student ID, MAC address, disk serial number; Step S41: When performing task inspection, obtain the MAC address and disk serial number of the host to be inspected; Step S42: After the task check is completed, the host MAC address and disk serial number information are added to the reported data; Step S43: After receiving the reported data, the server searches the fingerprint table for the student ID corresponding to the MAC address and disk serial number in the reported data; Step S44: If the student ID number in the reported data is consistent with the queried student ID number, the fingerprint server check passes; otherwise, the fingerprint server check fails.

9. The automatic scoring system for practical training tasks for Linux systems according to claim 1 is characterized in that: The process of the AI data analysis engine is as follows: Step S50: In response to receiving the data analysis instruction, the teacher inputs the content to be analyzed in the form of natural language; Step S51: In response to receiving the data analysis content input by the teacher, the AI data analysis engine reads the database table structure and generates SQL query statements and data visualization chart generation statements according to the teacher's request; Step S52: in response to receiving the SQL query statement, executing the SQL query statement in the database to obtain the query result; Step S53: in response to receiving the query result, executing a data visualization chart generation statement according to the query result to generate a visualization chart; Step S54: In response to receiving the visualization chart generation, the query results and the visualization chart are displayed on the page.

10. The automatic scoring system for practical training tasks for Linux systems according to claim 1 is characterized in that: The process of executing the task check rule by the rule execution engine is as follows: Step S60: Initialize the counter score, initialize the result list msg, and enter all task check rules into the queue; if the queue is empty, directly output the result; otherwise, go to step S61; Step S61: Pop the first checkpoint from the queue; execute the corresponding check code and record the output. If the output meets the expected result in the predefined parameter list, the counter score+score i , where score i For the score of the popped-up checkpoint, go to step S62; Otherwise go to step S63; Step S62: Check whether the queue is empty; if the queue is not empty, return to step S61 to process the next checkpoint; if the queue is empty, output the result; Step S63: Add an element to the result list, whose value is the error message of the current checkpoint; go to step S62.

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