A command line based interactive automated evaluation method and system

Abstract

The present invention discloses a command line-based interactive automated evaluation method and system, the evaluation method comprising obtaining a file to be evaluated, the file to be evaluated comprising image data or text data having a command line; parsing the image data or text data through OpenCV, a pre-trained BERT model, and a word segmenter; analyzing the integrity of the command line in the image data or text data through a command line integrity analyzer with preset rules; identifying keywords in the parsed text and accurately matching them with a user-defined keyword list; and generating an evaluation report according to preset security rules and evaluation criteria. The present invention can accurately extract images containing command line information, improve the accuracy of command line parsing, and thus improve the performance of the overall automated evaluation. At the same time, the introduction of the BERT model can better understand the meaning of the text command line, and can more accurately reflect the true meaning of the text, thereby improving the accuracy and precision of the evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of server evaluation, and more specifically to a command line-based interactive automated evaluation method and system. Background Art

[0002] With the rapid development of information technology, cloud computing and big data have become key technologies to promote the digital transformation of enterprises. As more and more enterprises migrate key business systems to servers, the importance of server automation evaluation has become increasingly prominent. However, traditional technical means often face problems of low efficiency and lack of accuracy when dealing with increasingly complex server environments; Specifically, the shortcomings of the prior art mainly include the following aspects: Text parsing limitations: Existing technologies often use fixed rule-based methods to parse text command lines. These methods are prone to errors or fail to parse correctly when faced with command lines that do not conform to predefined formats or syntax. In addition, existing technologies may lack the ability to understand and process natural languages, making it difficult to deal with command lines containing complex logic or conditional statements. Image recognition accuracy: When using traditional image processing algorithms to recognize command line content in images, they may not perform well under complex backgrounds, blurred images, or different font styles. Extensibility and compatibility: Existing systems may not be designed with sufficient consideration for future functional expansion or compatibility with other systems. This results in the need to refactor and adjust some codes when adapting to new servers and operating systems. In addition, existing systems may lack modular design, making functional updates and maintenance complex and inefficient. Summary of the invention

[0003] In this embodiment, a command line-based interactive automated evaluation method, system, electronic device, and storage medium are provided to solve the problems of low efficiency and insufficient accuracy in related technologies when dealing with increasingly complex server environments.

[0004] In a first aspect, an embodiment of the present invention provides a command line-based interactive automated evaluation method, the automated evaluation method comprising: Acquire a file to be evaluated, wherein the file to be evaluated includes image data or text data having a command line; Parse image data or text data using OpenCV, pre-trained BERT model, and word segmentation; Analyze the integrity of command lines in image data or text data through a command line integrity analyzer with preset rules; Identify keywords in the parsed text and perform exact matches on a user-defined keyword list; Generate an evaluation report based on preset security rules and evaluation criteria.

[0005] In an optional embodiment, the image data is parsed using OpenCV, a pre-trained BERT model, and a word segmenter, including: Load and preprocess the image through OpenCV, and apply feature detection algorithm to identify key points in the image; Generating a vector representation of the key points by using a descriptor extraction technique; using a feature matching algorithm to obtain the best match of the extracted descriptors; Based on the best match, feature points are drawn in the image.

[0006] In an optional embodiment, the text data is parsed using OpenCV, a pre-trained BERT model, and a word segmenter, including: Preprocess text data; Convert text data into tokenize through a tokenizer; The pre-trained BERT model is used to obtain an embedding representation of the text, and the nuances of the text command line are parsed based on the embedding representation.

[0007] In an optional embodiment, the integrity of the command line in the image data or text data is analyzed by a command line integrity analyzer with preset rules, including: Configure a rule set according to user requirements, the rule set is used to adapt to different command line environments and usage scenarios; Get the extracted command line string from the text data or image data; Performing a syntax check on the command line string; When the syntax check finds errors or omissions, corresponding error prompts or modification suggestions are generated.

[0008] In an optional embodiment, identifying keywords in the parsed text and performing an exact match with a user-defined keyword list includes: Keywords in the text are obtained, and the keywords are matched with a keyword list defined by a user to obtain matching results, wherein the matching results include related keywords, and corresponding tags are generated based on the related keywords.

[0009] In an optional embodiment, an evaluation report is generated according to preset security rules and evaluation criteria, including: Score the tags according to the preset safety rules and evaluation criteria to obtain the scoring results; An evaluation report is generated based on the scoring results and the correlation application description template.

[0010] Compared with the prior art, the command line-based interactive automated evaluation method of the present invention has the following beneficial effects: Through steps such as feature detection and descriptor extraction, key features in images can be more accurately identified, even in complex backgrounds or with poor image quality. For images containing command line information, image processing technology can be used to more accurately extract this information, thereby improving the accuracy of command line parsing. Through these steps, image data can be processed more intelligently to ensure that the information extracted from the image is accurate, thereby improving the performance of the overall automated evaluation.

[0011] By introducing natural language processing technology, it is possible to handle diverse input formats more flexibly, and even non-standard command lines can be parsed correctly. The introduction of the BERT model can better understand the meaning of text command lines, especially those containing complex logic or conditional statements. The text embedding representation obtained through deep learning algorithms can more accurately reflect the true meaning of the text, thereby improving the accuracy and precision of the evaluation.

[0012] In a second aspect, an embodiment of the present invention provides a command line-based interactive automated evaluation system, comprising: An upload module, used for uploading a file to be evaluated, wherein the file to be evaluated includes image data or text data with a command line; The image / text recognition module is configured to parse image data or text data through OpenCV, the pre-trained BERT model, and the word segmenter; The command line integrity analysis module determines the integrity of the command line through a command line integrity analyzer with preset rules; A keyword identification and matching module, which is used to identify keywords in the parsed text and accurately match them with a user-defined keyword list; and The evaluation report generation module is used to generate an evaluation report based on preset security rules and evaluation criteria.

[0013] In an optional embodiment, a visualization module is further included, and the visualization module is used to display the running status of the automated evaluation process.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a bus, wherein the processor, the communication interface and the memory communicate with each other through the bus, and the processor can call logic instructions in the memory to execute the steps of the method provided in the first aspect.

[0015] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the command line-based interactive automated evaluation method as described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the command line-based interactive automated evaluation system, electronic device, and storage medium of the present invention are the same as those of the command line-based interactive automated evaluation method described in the first aspect, so they will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 Flow chart of the command line-based interactive automated evaluation method in an embodiment of the present invention; Figure 2 It is a structural block diagram of a command line-based interactive automated evaluation system in an embodiment of the present invention; Figure 3 1 is a structural block diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0020] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with general skills in the technical field to which this application belongs. The words "one", "a", "the", "these" and the like in this application do not indicate a quantitative limitation, and they may be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone. Generally, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0021] First, the prior art and related concepts involved in the embodiments of the present invention are described: OpenCV: It contains a large number of functions for real-time video analysis, image recognition, object detection, camera calibration, stereo matching, etc., allowing developers to quickly build complex vision systems without having to write all the code from scratch; OpenCV is widely used in various fields, including face recognition, license plate recognition, medical image analysis, gesture recognition, robot navigation, etc. Due to its powerful functions and flexibility, OpenCV has become an important tool in academic research, teaching and commercial applications.

[0022] In an embodiment of the present invention, a command line based interactive automated evaluation method is provided. Figure 1 is a flowchart of the command line based interactive automated evaluation method of the present invention, such as Figure 1 As shown, the process includes the following steps: S1. Obtain a file to be evaluated, where the file to be evaluated includes image data or text data with a command line; S2, parse image data or text data through OpenCV, pre-trained BERT model and word segmentation; First, the image data is parsed using OpenCV, the pre-trained BERT model, and the word segmentor, including: The image is loaded and preprocessed through OpenCV, and the feature detection algorithm is applied to identify the key points in the image; it should be noted that preprocessing usually includes grayscale conversion, denoising, etc. First, the image data is loaded using the function of OpenCV, which is the first step in image processing, so as to facilitate further processing of the image.

[0023] Feature detection algorithms are applied to identify key points in the image. These key points are usually locations with unique properties in the image, such as corners, edges, etc., which help in subsequent image understanding and matching.

[0024] Descriptor extraction is used to generate vector representations of key points; feature point extraction is used to generate vector representations of key points, which can be used to describe the local image appearance around the feature points. Descriptor extraction is an important step in image recognition to facilitate understanding of the local characteristics of the image.

[0025] The best match of the extracted descriptors is obtained by using a feature matching algorithm; the best match of feature points between images or within an image is found by a feature matching algorithm. Feature matching helps to confirm the similarity between different images or the correspondence between different views of the same image.

[0026] Based on the best match, feature points are drawn in the image. When the best match is found, these feature points are drawn and displayed on the image. This step helps to visually show which parts are considered key features of the image. The marked feature points are then analyzed in depth to obtain deeper information, such as understanding the content or objects in the image, which is crucial for further applications such as recognition and parsing of command line instructions.

[0027] Specifically, the target image (image data) is loaded through the image reading function of OpenCV, and necessary preprocessing steps (such as grayscale conversion, denoising, etc.) are performed. Next, a feature detection algorithm (such as SIFT, SURF, or ORB) is applied to identify the key points in the image. In order to convert the key points in the image into a mathematically easy-to-handle form, a descriptor extraction technique is used to generate a vector representation of these key points. Subsequently, a feature matching algorithm (such as BFMatcher or FLANN) is used to compare the extracted descriptors with the known descriptors in the database to find the descriptor that is most similar to the extracted vector, that is, the best match. Finally, these feature points are drawn and displayed on the image. Through the training of a large machine learning model, key features can be quickly and accurately identified from the image, and in-depth analysis can be performed.

[0028] Through steps such as feature detection and descriptor extraction, key features in images can be more accurately identified, even in complex backgrounds or with poor image quality. For images containing command line information, image processing technology can be used to more accurately extract this information, thereby improving the accuracy of command line parsing. Through these steps, image data can be processed more intelligently to ensure that the information extracted from the image is accurate, thereby improving the performance of the overall automated evaluation.

[0029] Secondly, the text data is parsed through OpenCV, the pre-trained BERT model, and the word segmenter, including: Preprocessing of text data: The system preprocesses the input text data, such as removing noise, standardizing text format, removing stop words, etc., to facilitate subsequent processing. At this stage, tokenization may be used, which is to break the text into meaningful basic units, such as words or phrases.

[0030] The text data is converted into tokenize through the tokenizer; by loading the pre-trained BERT model and the tokenizer, the text data is converted into a format that the model can process, that is, tokenize. This means converting the original text into a series of digital vectors, each of which represents a word or sub-word.

[0031] Use the pre-trained BERT model to get the embedding representation of the text, and parse the nuances of the text command line based on the embedding representation. Use the BERT model to get the embedding representation of the text. The embedding representation can capture the semantic information and contextual relationships in the text to understand the nuances in the text command line. This is achieved by mapping each word to a vector in a multidimensional vector space. These vectors not only represent the meaning of the words themselves, but also reflect their position and role in the sentence.

[0032] With the embedded representation, we can start parsing the nuances in the text command line. The semantic understanding and context awareness provided by the BERT model can more accurately understand the intent in the command line and handle non-standard command lines with natural language features.

[0033] By introducing natural language processing technology, it is possible to handle diverse input formats more flexibly, and even non-standard command lines can be parsed correctly. The introduction of the BERT model can better understand the meaning of text command lines, especially those containing complex logic or conditional statements. The text embedding representation obtained through deep learning algorithms can more accurately reflect the true meaning of the text, thereby improving the accuracy and precision of the evaluation.

[0034] S3, analyzing the integrity of the command line in the image data or text data by a command line integrity analyzer with preset rules; It should be noted that the command line integrity analyzer can automatically detect and identify potential errors and omissions in the command line based on a series of preset rules by comparing the preset grammatical rules and logical structures. For example, if the file uploaded by the user is missing the cat / etc / passwd command, the analyzer will immediately prompt that the file is missing this command. At the same time, the analyzer allows users to configure rule sets according to specific needs to adapt to different command line environments and usage scenarios. Whether it is a logical error, a lack of necessary parameters, or a grammatical flaw, it can quickly identify and provide intuitive feedback.

[0035] The command line integrity analyzer uses preset rules to analyze the integrity of command lines in image data or text data, including: The rule set is configured according to the user's needs. The rule set is used to adapt to different command line environments and usage scenarios. Users configure the rule set according to specific needs. These rule sets can adapt to different command line environments and usage scenarios. For example, the rules can include common command formats, keywords that must exist, the correct order of parameters, etc.

[0036] Obtaining the extracted command line string from the text data or image data; extracting the command line string from the uploaded file or other source. That is, extracting the command line from the text data or image data. It should be noted that if it is extracted from an image, the text in the image data is first converted into a machine-readable form.

[0037] Perform syntax checking on command line strings to ensure that they follow pre-set syntax rules. If there are any parts of the command line that do not conform to the pre-set rules, these places are marked as potential errors. In addition to syntax checking, command lines are also logically validated to ensure that the command line is logically complete. For example, if a command requires a certain parameter and the actual command lacks this parameter, the command line integrity analyzer will identify this as a logical defect.

[0038] When the syntax check finds errors or omissions, the corresponding error prompts or modification suggestions are generated. If errors or omissions are detected in the command line, corresponding feedback information will be generated to point out the specific problem. These feedbacks can be direct error prompts or suggested modification opinions.

[0039] It should be noted that users can customize the rule set as needed to make it more suitable for specific tasks or environments. This ensures that the command line entered each time is in compliance with the specification, thereby improving work efficiency and avoiding the waste of time and resources caused by incorrect commands.

[0040] S4. Identify keywords in the parsed text and accurately match them with the user-defined keyword list. Through text analysis technology and image recognition technology, it can intelligently identify keywords in the text and accurately match them with the user-defined keyword list. For example, according to the keyword cat / etc / passwd, the user's username and password can be obtained. It is designed to quickly and accurately extract and match key information from large amounts of data.

[0041] Identify keywords in parsed text and perform exact matches against a user-defined list of keywords, including: Obtain keywords in the text, match the keywords with a user-defined keyword list, obtain matching results, the matching results include relevant keywords, and generate corresponding tags based on the relevant keywords.

[0042] Specifically, NLP technology is used to extract keywords from the text. This usually involves using statistical methods (such as TF-IDF) or deep learning-based methods (such as BERT) to determine which words are the most important or relevant, and matching the extracted keywords with a user-defined keyword list. If the extracted keywords match the keywords in the user-defined keyword list, they are considered to be relevant or objects that require special attention. This match can be based on simple string matching or advanced matching based on semantic similarity. Once the keywords are identified and matched with the user's definition, the system will generate corresponding tags. These tags can be used to further process the data, such as classification, sorting, or generating reports. Output the matching results, which can be used to help users quickly locate important information, reduce the time of manual screening of information, and ensure the quality and efficiency of information processing.

[0043] S5. Generate an evaluation report based on preset security rules and evaluation criteria. First, it extracts the required information from the files uploaded by the user, such as the user's username and password. Then search for evaluation items related to the username and password from the evaluation list. Each evaluation item is scored based on the security rules and judgment criteria set by experts; for example, for the item of whether the server identifies the logged-in user, if the username and password are obtained, then this item is scored as compliant, and if there is no username and password, this evaluation item is scored as non-compliant. The scoring rules for each evaluation item are formulated by professional evaluators, and the system determines the scoring results based on the rules formulated by the evaluators. Then, the module applies the description template to generate detailed evaluation results based on the scoring results and correlations. This module not only improves the quality and efficiency of decision-making and reduces human errors, but also ensures the accuracy and consistency of the evaluation process.

[0044] Specifically, an evaluation report is generated based on preset security rules and evaluation criteria, including: The tags are scored according to the preset security rules and evaluation criteria to obtain the scoring results; these tags may be part of a pre-defined rule set to describe command lines, system configuration items, or any other type of evaluation object. Each tag is scored based on the security rules and judgment criteria set by experts. These rules may include but are not limited to compliance checks, performance indicators, security requirements, etc. The scoring process usually involves considerations of multiple dimensions, such as the correctness of the command line, the security of the system configuration, etc.

[0045] Generate an evaluation report based on the scoring results and relevance using the description template. Based on the scoring results and relevance, the description template is used to generate detailed evaluation results. These results may include comprehensive scores, specific scores for each indicator, and suggestions for improvement. Finally, a detailed evaluation report is generated, which contains all relevant information collected during the entire evaluation process, as well as conclusions and suggestions based on the scoring rules.

[0046] The embodiment of the present invention also provides a command line-based interactive automated evaluation system, which is used to implement the above method embodiment, which has been described and will not be repeated. The terms "module", "unit", "subunit", etc. used below can implement a combination of software and / or hardware for predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware or a combination of software and hardware is also possible and conceivable.

[0047] like Figure 2 As shown, Figure 2 It is a structural block diagram of the command line-based interactive automated evaluation system of the present invention, which includes: The upload module 101 is used to upload a file to be evaluated, wherein the file to be evaluated includes image data or text data with a command line; The image / text recognition module 102 is configured to parse image data or text data through OpenCV, a pre-trained BERT model and a word segmenter; it should be noted that it is built in JAVA language, and the system calls the program through HTTP, integrating the powerful image processing function of OpenCV.

[0048] The BERT model is a deep learning model based on the Transformer architecture. It is pre-trained on a large-scale corpus, so it has good generalization and semantic understanding capabilities. At this stage, the BERT model converts the input text into a set of vector representations that contain the semantic information and contextual relationships in the text.

[0049] A command line integrity analysis module 103, which determines the integrity of the command line through a command line integrity analyzer with preset rules; Keyword recognition and matching module 104, used to recognize keywords in the parsed text and accurately match them with a user-defined keyword list; and The evaluation report generating module 105 is used to generate an evaluation report according to preset security rules and evaluation criteria.

[0050] It also includes a visualization module, which is used to display the running status of the automated evaluation process. Specifically, the visualization module has a visualization interface and provides an operation interface. The interface integrates the command line display area, file upload and management area, and the viewing and editing functions of the command line history. The user accesses the system interface, selects the text file or image file to be evaluated, and submits the file to the system by clicking the "Upload" button. The system automatically identifies the file type and prepares for subsequent processing. After receiving the uploaded file, the system automatically performs format detection and content parsing. As the above-mentioned parsing process, the details will not be repeated. The system further matches the extracted content with the built-in security rules and user-defined tags to ensure the accuracy and comprehensiveness of the evaluation. The system intelligently generates professional evaluation results based on the analysis results and matching rules.

[0051] Figure 3 A structural block diagram of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the following method: Acquire a file to be evaluated, wherein the file to be evaluated includes image data or text data having a command line; Parse image data or text data using OpenCV, pre-trained BERT model, and word segmentation; Analyze the integrity of command lines in image data or text data through a command line integrity analyzer with preset rules; Identify keywords in the parsed text and perform exact matches on a user-defined keyword list; Generate an evaluation report based on preset security rules and evaluation criteria.

[0052] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0053] An embodiment of the present invention further provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method provided in each of the above embodiments is implemented, for example, including: Acquire a file to be evaluated, wherein the file to be evaluated includes image data or text data having a command line; Parse image data or text data using OpenCV, pre-trained BERT model, and word segmentation; Analyze the integrity of command lines in image data or text data through a command line integrity analyzer with preset rules; Identify keywords in the parsed text and perform exact matches on a user-defined keyword list; Generate an evaluation report based on preset security rules and evaluation criteria.

[0054] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A command line based interactive automated evaluation method, characterized in that: The evaluation method includes: Acquire a file to be evaluated, wherein the file to be evaluated includes image data or text data having a command line; Parse image data or text data using OpenCV, pre-trained BERT model, and word segmentation; Analyze the integrity of command lines in image data or text data through a command line integrity analyzer with preset rules; Identify keywords in the parsed text and perform exact matches on a user-defined keyword list; Generate an evaluation report based on preset security rules and evaluation criteria.

2. The command line based interactive automated evaluation method according to claim 1, characterized in that: The image data is parsed using OpenCV, the pre-trained BERT model, and the word segmenter, including: Load and preprocess the image through OpenCV, and apply feature detection algorithm to identify key points in the image; Generating a vector representation of the key points by using a descriptor extraction technique; using a feature matching algorithm to obtain the best match of the extracted descriptors; Based on the best match, feature points are drawn in the image.

3. The command line based interactive automated evaluation method according to claim 1, characterized in that: Parse text data using OpenCV, pre-trained BERT model, and word segmenter, including: Preprocess text data; Convert text data into tokenize through a tokenizer; The pre-trained BERT model is used to obtain an embedding representation of the text, and the nuances of the text command line are parsed based on the embedding representation.

4. The command line based interactive automated evaluation method according to claim 1, characterized in that: The command line integrity analyzer uses preset rules to analyze the integrity of command lines in image data or text data, including: Configure a rule set according to user requirements, the rule set is used to adapt to different command line environments and usage scenarios; Get the extracted command line string from the text data or image data; Performing a syntax check on the command line string; When the syntax check finds errors or omissions, corresponding error prompts or modification suggestions are generated.

5. The command line based interactive automated evaluation method according to claim 1, characterized in that: Identify keywords in parsed text and perform exact matches against a user-defined list of keywords, including: Keywords in the text are obtained, and the keywords are matched with a keyword list defined by a user to obtain matching results, wherein the matching results include related keywords, and corresponding tags are generated based on the related keywords.

6. The command line based interactive automated evaluation method according to claim 5, characterized in that: Generate an evaluation report based on preset security rules and evaluation criteria, including: Score the tags according to the preset safety rules and evaluation criteria to obtain the scoring results; An evaluation report is generated based on the scoring results and the correlation application description template.

7. A command line based interactive automated evaluation system, characterized in that: include: An upload module, used for uploading a file to be evaluated, wherein the file to be evaluated includes image data or text data with a command line; The image / text recognition module is configured to parse image data or text data through OpenCV, the pre-trained BERT model, and the word segmenter; The command line integrity analysis module determines the integrity of the command line through a command line integrity analyzer with preset rules; A keyword identification and matching module, which is used to identify keywords in the parsed text and accurately match them with a user-defined keyword list; and The evaluation report generation module is used to generate an evaluation report based on preset security rules and evaluation criteria.

8. The command line based interactive automated evaluation system according to claim 7, characterized in that: It also includes a visualization module, which is used to display the running status of the automated evaluation process.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the command line-based interactive automated evaluation method is implemented as described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the command line-based interactive automated evaluation method as described in any one of claims 1 to 6 are implemented.

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