Intelligent test method and system for NVMe SSD

Abstract

The invention provides an intelligent test method and system for an NVMe SSD. The intelligent test method comprises the steps that a test requirement submitted by a user on a graphical user interface in a text input or file dragging mode is obtained; performing deep learning and semantic understanding on the test items by adopting a natural language processing algorithm to obtain key information; analyzing the key information through a deeply optimized file analysis algorithm to obtain test information; generating a test plan by using the test information and a preset test standard through an intelligent planning algorithm; automatically executing the test command according to the test plan to obtain a test result; analyzing and evaluating the test result through a comprehensive evaluation model to generate a test report; a user can initiate a test process through an input or drag-in operation, so that the technical threshold of use is greatly reduced; manual operation is reduced through the automatic testing process, and the testing efficiency is remarkably improved; various personalized test requirements of users can be met, and the accuracy of test results is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hard disk testing, and in particular to an intelligent testing method and system for NVMe SSDs. Background Art

[0002] With the development of the digital age, NVMe SSD, as a new generation of high-performance data storage devices, has been widely adopted in many fields. Its high-speed data transmission capability and low latency characteristics provide strong support for processing massive data. But at the same time, different application scenarios have put forward diverse and strict requirements on the performance and reliability of NVMe SSD.

[0003] Existing NVMe SSD testing methods often have many drawbacks. On the one hand, the operation of the test tool is extremely complicated, and the user is required to have a deep professional knowledge reserve and rich technical practice experience when setting various test parameters and conditions. This undoubtedly brings great trouble to ordinary users and makes them feel difficult during the testing process. On the other hand, the test method lacks flexibility and is difficult to customize according to the specific needs of users. It cannot meet the unique requirements of diversified application scenarios and the personalized testing goals of users. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an intelligent testing method and system for NVMe SSD. Users can initiate the testing process by inputting or dragging operations, which greatly reduces the technical threshold for use; the automated testing process reduces manual operations and significantly improves the testing efficiency; it can meet users' various personalized testing needs and improve the accuracy of test results.

[0005] In a first aspect, an embodiment of the present invention provides an intelligent testing method for NVMe SSD, the method comprising:

[0006] Acquire a test requirement submitted by a user on a graphical user interface by text input or by dragging a file; wherein the test requirement includes a test item;

[0007] Using a natural language processing algorithm to perform deep learning and semantic understanding on the test items to obtain key information; wherein the key information includes the test type, the amount of test data, and the test duration;

[0008] Parsing the key information through a deeply optimized file parsing algorithm to obtain test information;

[0009] Generate a test plan by using an intelligent planning algorithm to generate the test information and the preset test standards; wherein the test plan includes test steps, test parameters and test sequence;

[0010] Automatically execute test commands according to the test plan to obtain test results;

[0011] The test results are analyzed and evaluated through a comprehensive evaluation model to generate a test report.

[0012] Furthermore, the test information and the preset test standards are used through an intelligent planning algorithm to generate a test plan, including:

[0013] The test influencing factors are classified and predicted through the optimization strategy in machine learning to obtain different classification results; among them, the test influencing factors include the test environment, hardware resources and test priority;

[0014] During the test parameter optimization process, the changes in the test environment are monitored in real time to obtain dynamic information of the test environment;

[0015] Based on the dynamic information of the test environment, the test parameters are dynamically adjusted through the intelligent planning algorithm, and adapted according to the inherent characteristics of the NVMe SSD, so as to generate the test plan.

[0016] Furthermore, the method further comprises:

[0017] Compare and analyze the test plan with historical successful test plans to obtain analysis results;

[0018] The test plan is optimized and adjusted according to the analysis results until the test plan meets the required conditions.

[0019] Furthermore, the method further comprises:

[0020] In the process of automatically executing the test command, various key data and equipment status are monitored and collected in real time through the real-time monitoring system to obtain valuable information;

[0021] Obtaining potential problem information from the value information;

[0022] An early warning is issued based on the potential problem information.

[0023] Furthermore, the test results are analyzed and evaluated through a comprehensive evaluation model to generate a test report, including:

[0024] The test results are analyzed and evaluated using big data analysis algorithms and machine learning algorithms, and a visual test report is generated using a front-end chart library.

[0025] In a second aspect, an embodiment of the present invention provides an intelligent testing system for NVMe SSD, the system comprising:

[0026] A user interaction module, used to obtain test requirements submitted by a user on a graphical user interface by text input or by dragging and dropping a file; wherein the test requirements include test items;

[0027] A test item parsing module is used to perform deep learning and semantic understanding of the test items using a natural language processing algorithm to obtain key information; the key information is parsed using a deep optimized file parsing algorithm to obtain test information; wherein the key information includes the test type, the amount of test data, and the test duration;

[0028] An intelligent test planning module, used to generate a test plan by applying the test information and preset test standards to an intelligent planning algorithm; wherein the test plan includes test steps, test parameters and test sequence;

[0029] A test execution module is used to automatically execute test commands according to the test plan to obtain test results;

[0030] The result evaluation and feedback module is used to analyze and evaluate the test results through a comprehensive evaluation model and generate a test report.

[0031] Furthermore, the intelligent test planning module is specifically used for:

[0032] The test influencing factors are classified and predicted through the optimization strategy in machine learning to obtain different classification results; among them, the test influencing factors include the test environment, hardware resources and test priority;

[0033] During the test parameter optimization process, the changes in the test environment are monitored in real time to obtain dynamic information of the test environment;

[0034] Based on the dynamic information of the test environment, the test parameters are dynamically adjusted through the intelligent planning algorithm, and adapted according to the inherent characteristics of the NVMe SSD, so as to generate the test plan.

[0035] Furthermore, the test execution module is specifically used for:

[0036] Compare and analyze the test plan with historical successful test plans to obtain analysis results;

[0037] The test plan is optimized and adjusted according to the analysis results until the test plan meets the required conditions.

[0038] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the method described above when executing the computer program.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having a non-volatile program code executable by a processor, wherein the program code enables the processor to execute the method as described above.

[0040] The embodiment of the present invention provides an intelligent testing method and system for NVMe SSD, including: obtaining a test requirement submitted by a user on a graphical user interface by text input or dragging a file; wherein the test requirement includes test items; using a natural language processing algorithm to perform deep learning and semantic understanding on the test items to obtain key information; wherein the key information includes test type, test data volume and test duration; parsing the key information through a deeply optimized file parsing algorithm to obtain test information; generating a test plan through an intelligent planning algorithm for the test information and preset test standards; wherein the test plan includes test steps, test parameters and test sequence; automatically executing test commands according to the test plan to obtain test results; analyzing and evaluating the test results through a comprehensive evaluation model to generate a test report; a user can initiate a test process by inputting or dragging in operations, which greatly reduces the technical threshold for use; the automated test process reduces manual operations and significantly improves test efficiency; it can meet a variety of personalized test needs of users and improve the accuracy of test results.

[0041] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of an intelligent testing method for NVMe SSD provided in Embodiment 1 of the present invention;

[0045] Figure 2 This is a flowchart of step S104 in the intelligent testing method for NVMe SSD provided in the first embodiment of the present invention;

[0046] Figure 3 Schematic diagram of the intelligent testing system for NVMe SSD provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0048] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0049] Embodiment 1:

[0050] Figure 1 This is a flow chart of the intelligent testing method for NVMe SSD provided in Example 1 of the present invention.

[0051] Reference Figure 1 , the method comprises the following steps:

[0052] Step S101, obtaining a test requirement submitted by a user on a graphical user interface by text input or by dragging a file; wherein the test requirement includes test items;

[0053] Specifically, the graphical user interface is a user-friendly and intuitive interface. It not only supports users to manually input clear and concise text, but also allows them to directly drag in files containing test items, so as to submit test requirements conveniently and efficiently. It adopts intelligent guided input technology, which can provide relevant prompts and suggestions in real time according to the user's initial operation, helping users to express test intentions accurately.

[0054] In the prior art, users usually need to set test parameters through complex command line interfaces or professional software, which is a high threshold for non-professionals. This application provides an intuitive graphical user interface (GUI) that supports users to submit test requirements by simple text input or dragging files, and has intelligent guided input technology, which greatly reduces the difficulty of operation.

[0055] This application uses modern UI frameworks such as React or Vue.js to build a responsive interface. It uses HTML5's drag-and-drop API and text input API to achieve user-friendly interaction.

[0056] Step S102, using a natural language processing algorithm to perform deep learning and semantic understanding on the test items to obtain key information; wherein the key information includes the test type, the amount of test data, and the test duration;

[0057] Step S103, parsing the key information through a deeply optimized file parsing algorithm to obtain test information;

[0058] Specifically, the present application uses natural language processing algorithms and deeply optimized file parsing algorithms to conduct all-round, multi-level in-depth analysis and thorough understanding of the test items entered or dragged in by the user.

[0059] In the process of extracting key information, such as test type (covering multiple dimensions such as read and write performance, random access, durability, etc.), test data volume and test duration, a semantic understanding model based on deep learning is adopted, which can accurately identify the potential needs and nuances in user expressions, greatly improving the accuracy and completeness of information extraction.

[0060] The existing technology usually relies on manual interpretation of test requirements, which is inefficient and prone to errors. This application uses natural language processing algorithms (NLP) to automatically parse test items, improving parsing speed and accuracy.

[0061] In NLP technology, BERT or GPT models are used to perform deep learning and semantic understanding of natural language text input by users; in file parsing, XML / JSON parsers are used to quickly extract structured data in files to obtain test information.

[0062] Step S104, generating a test plan by using an intelligent planning algorithm to process the test information and the preset test standards; wherein the test plan includes test steps, test parameters and test sequence;

[0063] Based on the analyzed test information, the inherent characteristics of NVMe SSD and the authoritative preset test standards, a detailed and scientifically reasonable test plan is automatically generated through an intelligent planning algorithm.

[0064] The intelligent planning algorithm integrates the optimization strategies in machine learning and can comprehensively consider various factors, such as hardware resource allocation, test priority sorting, etc., to ensure the efficiency and rationality of the test plan. Specifically, it includes carefully designed test steps, accurately set test parameters, and scientifically arranged test sequence.

[0065] In the prior art, test plans are usually manually formulated, lacking flexibility and personalization. This application automatically generates a personalized test plan based on the characteristics and test standards of NVMe SSD.

[0066] Step S105, automatically executing the test command according to the test plan to obtain the test result;

[0067] Specifically, Python's subprocess module or Shell script is used in the automation script to automatically execute the test command according to the test plan, and the NVMe management interface (NVMe-MI) is used to monitor the data and status of the test process in real time.

[0068] Step S106, analyzing and evaluating the test results through a comprehensive evaluation model to generate a test report.

[0069] Further, see Figure 2 , step S104 includes the following steps:

[0070] Step S201, classifying and predicting the test influencing factors through the optimization strategy in machine learning to obtain different classification results; wherein the test influencing factors include the test environment, hardware resources and test priority;

[0071] Here, we use machine learning techniques such as decision trees in optimization strategies to classify and predict factors such as test environment, hardware resources, and test priority. Based on different classification results, we provide accurate decision support for the generation of test plans.

[0072] Step S202, during the test parameter optimization process, monitor the changes of the test environment in real time to obtain dynamic information of the test environment;

[0073] Step S203, based on the dynamic information of the test environment, dynamically adjust the test parameters through the intelligent planning algorithm, and adapt according to the inherent characteristics of the NVMe SSD to generate a test plan.

[0074] Specifically, during the test parameter optimization process, the test environment changes are monitored in real time, such as hardware performance fluctuations and temperature changes. Based on these dynamic test environment information, the test parameters are dynamically adjusted using intelligent planning algorithms to ensure the accuracy and reliability of the test.

[0075] For NVMe SSDs of different models and characteristics, the intelligent planning algorithm can perform personalized adaptation according to their inherent characteristics. For example, for models with faster read and write speeds, the intensity of stress testing can be appropriately increased; for models with higher stability requirements, the weight of durability testing can be increased.

[0076] Furthermore, the method further comprises the following steps:

[0077] Step S301, compare and analyze the test plan with the historical test success plan to obtain the analysis result;

[0078] Step S302, optimize and adjust the test plan according to the analysis results until the test plan meets the requirements.

[0079] Specifically, in each step of generating the test plan, continuous evaluation and verification are carried out. The generated test plan is compared and analyzed with the historical test success plan to timely identify potential deficiencies and make optimization adjustments until the final detailed and scientifically reasonable test plan is generated.

[0080] Furthermore, the method further comprises the following steps:

[0081] Step S401, during the process of automatically executing the test command, various key data and device status are monitored and collected in real time through the real-time monitoring system to obtain valuable information;

[0082] Step S402, obtaining potential problem information from the value information;

[0083] Step S403: issuing an early warning based on the potential problem information.

[0084] Specifically, according to the generated test plan, high-precision control technology is used to accurately and accurately perform actual test operations on NVMe SSDs. During the test, the real-time monitoring system is used to continuously monitor and collect various key data and device status during the test.

[0085] The real-time monitoring system uses advanced sensor fusion technology and data filtering algorithms, which can quickly filter out valuable information from massive data, promptly detect potential problem information and issue early warnings.

[0086] In the prior art, test execution often relies on manual operation, which not only increases the possibility of human error, but also limits the frequency and continuity of the test. Operators may interrupt the test or record inaccurate data due to fatigue or negligence. The present application automates the test execution, reduces human error, and improves the accuracy and stability of the test.

[0087] Further, step S106 includes:

[0088] The test results are analyzed and evaluated using big data analysis algorithms and machine learning algorithms, and a visual test report is generated using the front-end chart library.

[0089] Specifically, the test results obtained by the test execution are comprehensively evaluated and analyzed using a comprehensive evaluation model. This comprehensive evaluation model integrates a variety of data analysis methods and statistical principles, and can accurately generate clear, easy-to-understand, and detailed test reports to provide users with comprehensive feedback on the performance of NVMe SSDs. At the same time, based on the results of the in-depth analysis, intelligent recommendation technology is used to provide users with highly targeted and practical optimization suggestions.

[0090] In the prior art, test result analysis is mostly static reports, which are difficult to provide real-time feedback. This application provides dynamic test reports and real-time performance feedback to help users adjust test strategies in a timely manner.

[0091] Among them, big data analysis algorithms and machine learning algorithms, such as cluster analysis, are used to conduct in-depth analysis of the test results, and front-end chart libraries such as D3.js or ECharts are used to generate intuitive and visual test reports.

[0092] This application uses cutting-edge deep learning natural language processing algorithms and deeply optimized file parsing algorithms to deeply explore the potential semantics and subtle features of user testing needs, ensuring accurate understanding of users' diverse testing needs.

[0093] Through intelligent test planning algorithms, which integrate advanced strategies in machine learning and knowledge of NVMe SSD characteristics, it can dynamically generate highly adaptive, efficient and reasonable test plans based on user needs.

[0094] It adopts high-precision and real-time test execution and monitoring technology, combined with advanced sensor technology and data processing algorithms, to accurately control the test process, collect key data in real time and conduct in-depth analysis, effectively ensuring the accuracy and reliability of test results.

[0095] Embodiment 2:

[0096] Figure 3 Schematic diagram of the intelligent testing system for NVMe SSD provided in Embodiment 2 of the present invention.

[0097] Reference Figure 3 The system comprises:

[0098] A user interaction module is used to obtain test requirements submitted by a user on a graphical user interface by text input or by dragging and dropping a file; wherein the test requirements include test items;

[0099] The test item parsing module is used to perform deep learning and semantic understanding of the test items using a natural language processing algorithm to obtain key information; the key information is parsed using a deeply optimized file parsing algorithm to obtain test information; wherein the key information includes the test type, the amount of test data, and the test duration;

[0100] An intelligent test planning module is used to generate a test plan by using an intelligent planning algorithm to combine test information and preset test standards; wherein the test plan includes test steps, test parameters and test sequence;

[0101] The test execution module is used to automatically execute the test commands according to the test plan and obtain the test results;

[0102] The result evaluation and feedback module is used to analyze and evaluate the test results through a comprehensive evaluation model and generate a test report.

[0103] In this embodiment, the user inputs the test items through the user interaction module with an intuitive and concise operation method, or easily drags in relevant files. In this process, the intelligent guidance technology will assist the user in real time to ensure the accuracy and completeness of the input.

[0104] The test item parsing module responds quickly, using advanced semantic understanding models and parsing algorithms to conduct in-depth analysis of user-submitted content, accurately extracting key test information without missing any subtle requirements or key points.

[0105] After receiving the analysis results, the intelligent test planning module immediately starts the intelligent planning algorithm, comprehensively considers various factors, and quickly generates a detailed, comprehensive, scientific and reasonable test plan to provide clear guidance for subsequent test operations.

[0106] The test execution module uses high-precision control technology and real-time monitoring technology based on the generated test plan to methodically and accurately test the NVMe SSD, ensuring the stability and accuracy of the test process while collecting and recording key data and status information in real time.

[0107] The result evaluation and feedback module conducts a comprehensive and in-depth analysis and evaluation of the test results, generates a detailed test report using a comprehensive evaluation model, and provides users with practical optimization suggestions through intelligent recommendation technology, helping users to fully understand the performance of NVMe SSDs and providing strong support for their subsequent use and optimization.

[0108] This application can automate the test process to reduce manual operations and significantly improve test efficiency; the intuitive GUI and natural language processing technology allow non-professionals to easily perform tests, thereby lowering the threshold; NLP and automated testing reduce human errors and improve the accuracy of test results; the intelligent test planning module can generate personalized test plans based on NVMe SSD characteristics and test history, improving the pertinence and effectiveness of the test.

[0109] Furthermore, the intelligent test planning module is specifically used for:

[0110] The test influencing factors are classified and predicted through the optimization strategy in machine learning to obtain different classification results; among them, the test influencing factors include the test environment, hardware resources and test priority;

[0111] During the test parameter optimization process, the changes in the test environment are monitored in real time to obtain dynamic information about the test environment;

[0112] Based on the dynamic information of the test environment, the test parameters are dynamically adjusted through the intelligent planning algorithm, and adapted according to the inherent characteristics of the NVMe SSD to generate a test plan.

[0113] Furthermore, the test execution module is specifically used for:

[0114] Compare and analyze the test plan with the historical successful test plan to obtain the analysis results;

[0115] Optimize and adjust the test plan based on the analysis results until the test plan meets the requirements.

[0116] The beneficial effects of this application are:

[0117] The operation is extremely simple: users do not need to have deep professional knowledge reserves, they can easily initiate the test process by simply inputting or dragging operations, which greatly reduces the technical threshold for use and achieves "zero threshold" for test operations.

[0118] Highly customized: It can accurately meet users' various personalized testing needs, flexibly adapt to various application scenarios and diverse testing objectives, and truly "tailor-made".

[0119] Significantly improve test efficiency: With the ability to intelligently generate test plans and efficiently execute tests, it greatly saves users' time spent on manual settings and tedious operations, achieving a leapfrog improvement in overall test efficiency.

[0120] The results are accurate and reliable: The introduction of advanced testing technology and in-depth data analysis methods provides a solid guarantee for the accuracy and reliability of the test results, and presents users with a valuable reference.

[0121] Provide practical optimization suggestions: Based on the test results, we can provide users with highly targeted optimization suggestions, effectively helping to improve the performance of NVMe SSDs and significantly extend their service life.

[0122] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent testing method for the NVMe SSD provided in the above embodiment are implemented.

[0123] An embodiment of the present invention also provides a computer-readable medium having a non-volatile program code executable by a processor, wherein a computer program is stored on the computer-readable medium, and when the computer program is executed by the processor, the steps of the intelligent testing method for the NVMe SSD of the above embodiment are executed.

[0124] The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0126] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0127] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. 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 methods 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., which can store program codes.

[0128] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0129] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An intelligent testing method for NVMe SSD, characterized in that: The method comprises: Acquire a test requirement submitted by a user on a graphical user interface by text input or by dragging a file; wherein the test requirement includes a test item; Using a natural language processing algorithm to perform deep learning and semantic understanding on the test items to obtain key information; wherein the key information includes the test type, the amount of test data, and the test duration; Parsing the key information through a deeply optimized file parsing algorithm to obtain test information; Generate a test plan by using an intelligent planning algorithm to generate the test information and the preset test standards; wherein the test plan includes test steps, test parameters and test sequence; Automatically execute test commands according to the test plan to obtain test results; The test results are analyzed and evaluated through a comprehensive evaluation model to generate a test report.

2. The intelligent testing method for NVMe SSD according to claim 1, characterized in that: The test information and the preset test standards are used through an intelligent planning algorithm to generate a test plan, including: The test influencing factors are classified and predicted through the optimization strategy in machine learning to obtain different classification results; among them, the test influencing factors include the test environment, hardware resources and test priority; During the test parameter optimization process, the changes in the test environment are monitored in real time to obtain dynamic information of the test environment; Based on the dynamic information of the test environment, the test parameters are dynamically adjusted through the intelligent planning algorithm, and adapted according to the inherent characteristics of the NVMe SSD, so as to generate the test plan.

3. The intelligent testing method for NVMe SSD according to claim 2, characterized in that: The method further comprises: Compare and analyze the test plan with historical successful test plans to obtain analysis results; The test plan is optimized and adjusted according to the analysis results until the test plan meets the required conditions.

4. The intelligent testing method for NVMe SSD according to claim 1, characterized in that: The method further comprises: In the process of automatically executing the test command, various key data and equipment status are monitored and collected in real time through the real-time monitoring system to obtain valuable information; Obtaining potential problem information from the value information; An early warning is issued based on the potential problem information.

5. The intelligent testing method for NVMe SSD according to claim 1, characterized in that: The test results are analyzed and evaluated through a comprehensive evaluation model to generate a test report, including: The test results are analyzed and evaluated using big data analysis algorithms and machine learning algorithms, and a visual test report is generated using a front-end chart library.

6. An intelligent testing system for NVMe SSD, characterized in that: The system comprises: A user interaction module, used to obtain test requirements submitted by a user on a graphical user interface by text input or by dragging and dropping a file; wherein the test requirements include test items; A test item parsing module is used to perform deep learning and semantic understanding of the test items using a natural language processing algorithm to obtain key information; the key information is parsed using a deep optimized file parsing algorithm to obtain test information; wherein the key information includes the test type, the amount of test data, and the test duration; An intelligent test planning module, used to generate a test plan by applying the test information and preset test standards to an intelligent planning algorithm; wherein the test plan includes test steps, test parameters and test sequence; A test execution module is used to automatically execute test commands according to the test plan to obtain test results; The result evaluation and feedback module is used to analyze and evaluate the test results through a comprehensive evaluation model and generate a test report.

7. The intelligent testing system for NVMe SSD according to claim 6, characterized in that: The intelligent test planning module is specifically used for: The test influencing factors are classified and predicted through the optimization strategy in machine learning to obtain different classification results; among them, the test influencing factors include the test environment, hardware resources and test priority; During the test parameter optimization process, the changes in the test environment are monitored in real time to obtain dynamic information of the test environment; Based on the dynamic information of the test environment, the test parameters are dynamically adjusted through the intelligent planning algorithm, and adapted according to the inherent characteristics of the NVMe SSD, so as to generate the test plan.

8. The intelligent testing system for NVMe SSD according to claim 7, characterized in that: The test execution module is specifically used for: Compare and analyze the test plan with historical successful test plans to obtain analysis results; The test plan is optimized and adjusted according to the analysis results until the test plan meets the required conditions.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

10. A computer readable medium having a non-volatile program code executable by a processor, characterized in that: The program code enables the processor to execute the method according to any one of claims 1 to 5.

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