An optimization method, system, device and medium of a whole machine system diagnosis platform

Abstract

The application provides an optimization method, system and device of a whole machine system diagnosis platform and a medium. The method comprises the following steps: splitting the whole machine system diagnosis platform into a system layer and a business layer; constructing a new database system for the whole machine system diagnosis platform, wherein the new database system comprises a relational database and a non-relational database; modifying the deployment form of the business layer program code; collecting the operation data of a machine to be detected through the whole machine system diagnosis platform, and inputting a trained diagnosis model to perform fault diagnosis. The application can greatly reduce the frequency of program deployment caused by Hotfix, and reduces the workload of operation and maintenance and the risk of production abnormalities.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to an optimization method, system, device, and medium for a whole-system diagnostic platform. Background Technology

[0002] With the rapid development of the modern internet, especially driven by the demands of "big data" and "artificial intelligence" propelling the high-speed growth of the digital economy, the demand for servers is also growing exponentially. Furthermore, to meet the diverse customized needs of different customers, server manufacturers are launching various models with both standard and non-standard configurations. On the other hand, various industries are placing increasingly higher demands on server operational stability and concurrent processing capabilities, particularly the financial and internet industries, which require server diagnostics, testing, and repair services to be completed within a short timeframe and under heavy load. The large scale and diverse configurations of servers present significant challenges in R&D, production, and fault diagnosis. Manual inspection is time-consuming and cannot cover all aspects. Therefore, during server production, a systematic and comprehensive diagnostic and inspection of the entire server is necessary to eliminate all faults and anomalies before shipment. Regarding server system diagnostics, from the initial script-based and tool-based maintenance to platform-based maintenance, scenarios requiring human intervention still occupy a significant portion, demanding high levels of experience from maintenance personnel and involving a heavy workload. Faced with increasingly customized demands and rising labor costs, the requirements for automated system fault diagnosis cannot be met.

[0003] A major pain point currently facing server system diagnostic platforms is their high cost and poor timeliness. Problems encountered during system diagnostics are primarily based on manual analysis and experience-based judgment, which relies heavily on R&D and operations personnel. A significant portion of these faults, even after clarification, require feedback to the diagnostic system for correction and adaptation. From fault analysis to system hot-fix, it's a lengthy and somewhat risky process. For diagnostic platforms, regular system upgrades are no longer sufficient to diagnose various customized configurations. The numerous hot-fixes interspersed between regular upgrades contradict the principles of automated and intelligent system diagnostics.

[0004] Currently, the most common deployment method for system diagnostic platforms is to place the diagnostic program source code as a file on a server. During production, the machine under test downloads the source code file via a local area network and then executes the diagnostic program. The source code of the system diagnostic platform is managed using version control tools, and system upgrades and maintenance require strict control according to procedures. When a machine under test malfunctions, R&D or maintenance personnel intervene to identify and clarify the problem. If the machine needs to pass through the diagnostic platform, a hotfix (commonly known as online repair) is required. This process is relatively time-consuming. Between regular system maintenance sessions, multiple hotfixes are often performed, posing a certain risk to the production environment and potentially causing large-scale production interruptions.

[0005] It is evident that, in response to the current pain points in server system diagnosis, how to quickly improve the accuracy and effectiveness of server fault handling by the whole system diagnostic platform, reduce operation and maintenance costs, and improve diagnostic efficiency is a new issue that urgently needs to be addressed. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide an optimization method, system, device, and medium for a whole-system diagnostic platform, which can significantly reduce the frequency of program deployment caused by hotfixes, thereby reducing the workload of operation and maintenance and the risk of production anomalies.

[0007] To achieve the above objectives, this invention provides the following technical solution: an optimization method for a whole-system diagnostic platform, comprising:

[0008] The whole system diagnostic platform is divided into a system layer and a business layer;

[0009] A new database system will be built for the whole system diagnostic platform. The new database system includes relational databases and non-relational databases.

[0010] Modify the deployment method of the business layer program code;

[0011] The system diagnostic platform collects the operating data of the machine under test and inputs it into the trained diagnostic model for fault diagnosis.

[0012] Furthermore, the process of splitting the whole system diagnostic platform into a system layer and a business layer includes:

[0013] A system layer is set up in the whole system diagnostic platform to execute and monitor the customized scripts deployed by the operation and maintenance personnel, monitor the system diagnostic process, collect basic information of the server's preset components during the server detection process, provide real-time feedback on the server's operating status, and collect system logs.

[0014] Furthermore, the method of splitting the whole system diagnostic platform into a system layer and a business layer also includes:

[0015] The source code of the system layer is solidified into a set of solidified programs that are stored in files, managed by version control tools, and regularly maintained and upgraded, and stored in a central server.

[0016] Furthermore, the process of splitting the whole system diagnostic platform into a system layer and a business layer includes:

[0017] A business layer is set up in the whole system diagnostic platform. The business layer adopts a diagnostic and inspection program that runs on the system layer to obtain component configuration and firmware version information, measure component sensor information indicators, perform component stress tests, and troubleshoot system and component logs.

[0018] Furthermore, the relational database uses Oracle or MySQL to store the code of business layer scripts and programs, and to store the version information of the code; the non-relational database uses the document-oriented database MongoDB to store component information, real-time status, and document data generated during the operation of the machine under test.

[0019] Furthermore, the proposed modification of the deployment format of the business layer program code includes: decoupling the functions of the system layer and the business layer; after decoupling, storing the source code of the system layer in files on a central server; developing a management module on top of the system layer, introducing an online code editor framework, and using this editor to develop, check, and test operation and maintenance scripts; storing the code of the version control function components in binary form in a new database system; extracting diagnostic rules and indicators, stress test thresholds, and blacklists and whitelists of system logs, adjusting the extracted indicator data based on research into server production diagnostics, and storing it in the management module.

[0020] Furthermore, the step of collecting operational data of the machine under test through the whole system diagnostic platform and inputting it into the trained diagnostic model for fault diagnosis includes:

[0021] The system diagnostic platform collects operational data of the machine under test, including component configuration information, firmware version information, performance indicators, stress test data, generated system logs, and BMC SEL logs during the production process. The operational data is used as the operational features for fault diagnosis and input into the trained diagnostic model for fault diagnosis, and the diagnostic results are output. The diagnostic results are collected, classified, and the failure rate of individual faults in each stage of each configuration model is calculated.

[0022] Accordingly, the present invention also discloses an optimization system for a whole system diagnostic platform, comprising:

[0023] The functional decomposition module is used to split the whole system diagnostic platform into a system layer and a business layer;

[0024] The database construction module is used to build a new database system for the whole system diagnostic platform. The new database system includes relational databases and non-relational databases.

[0025] The deployment module is used to modify the deployment method of business layer program code;

[0026] The fault diagnosis module is used to collect the operating data of the machine under test through the whole system diagnostic platform and input it into the trained diagnostic model for fault diagnosis.

[0027] Accordingly, this invention discloses an optimization device for a whole system diagnostic platform, comprising:

[0028] The memory is used to store the optimization program of the whole system diagnostic platform;

[0029] The processor is configured to implement the steps of the optimization method for the system diagnostic platform as described above when executing the optimization program of the system diagnostic platform.

[0030] Accordingly, the present invention discloses a readable storage medium storing an optimization program for a whole system diagnostic platform, wherein when the optimization program for the whole system diagnostic platform is executed by a processor, it implements the steps of the optimization method for the whole system diagnostic platform as described in any of the above claims.

[0031] Compared with existing technologies, the advantages of this invention are as follows: This invention provides an optimized method, system, device, and medium for a whole-system diagnostic platform. Based on traditional system diagnostic platforms, it abstracts various diagnostic functions, performance indicators, stress thresholds, and fault blacklists and whitelists, storing them in binary form in the platform database. Maintenance is achieved through an independent web function, offering flexible maintenance and deployment, immediate effectiveness, and independence from or impact on overall system upgrades and iterations. Fault information and system logs are collected and analyzed during production to provide standardized guidance for safe production. This invention reduces the frequency of hotfixes on the whole-system diagnostic platform, enhances platform maintainability, improves operational efficiency, and reduces operational risks.

[0032] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention.

[0035] Figure 2 This is a system structure diagram of a specific embodiment of the present invention.

[0036] In the diagram, 1 is the functional breakdown module; 2 is the database construction module; 3 is the deployment module; and 4 is the fault diagnosis module. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1:

[0039] like Figure 1 As shown, this embodiment provides an optimization method for a whole system diagnostic platform, including the following steps:

[0040] S1: The whole system diagnostic platform is split into a system layer and a business layer.

[0041] As an example, a system layer is set up in the whole system diagnostic platform to execute and monitor customized scripts deployed by maintenance personnel, monitor the system diagnostic process, collect basic information of preset server components during server testing, provide real-time feedback on server operating status, and collect system logs. The source code of the system layer is solidified as a set of fixed programs stored in files, managed through version control tools, and regularly maintained and upgraded, and stored on a central server. Simultaneously, a business layer is set up in the whole system diagnostic platform. This business layer uses diagnostic and inspection programs running on top of the system layer to obtain component configuration and firmware version information, measure component sensor information indicators, perform component stress testing, and troubleshoot system and component log faults.

[0042] Specifically:

[0043] From a functional perspective, the whole system diagnostic platform is divided into a system layer and a business layer. The system layer refers to the underlying logic that supports the stable operation of the entire system. It is responsible for executing and monitoring customized scripts deployed by operations and maintenance personnel, monitoring the system diagnostic process, collecting basic information about various server components (CPU, GPU, memory, hard drive, etc.) during server testing, providing real-time feedback on the server's operating status, and collecting various system logs. Because the system layer's functionality is relatively stable and its iteration frequency is low, the system layer's source code is solidified into a set of fixed programs stored in files, managed through version control tools, and regularly maintained and upgraded, and stored on a central server.

[0044] The business layer refers to a series of diagnostic and inspection programs running above the system layer. These include, but are not limited to, component configuration and firmware version information, measuring component sensor information metrics, component stress testing, system and component log troubleshooting, and other customized checks. Different server models, or even servers of the same model but with different configurations, often have different components and firmware versions. Furthermore, the same component may have different models and generations, so the checks for these aspects may also differ slightly. To address these issues, the testing programs need to be frequently adjusted to adapt to different situations. By analyzing and identifying the functions of the whole system diagnostic platform, the system layer and business layer functions are separated and decoupled, making it easier for operations and maintenance personnel to maintain the business layer functions.

[0045] S2: Build a new database system for the whole system diagnostic platform. The new database system includes relational databases and non-relational databases.

[0046] The relational database uses Oracle or MySQL to store the code of the business layer scripts and programs, as well as the version information of the code; the non-relational database uses the document-oriented database MongoDB to store the component information, real-time status, and document data generated during the operation of the machine under test.

[0047] Specifically:

[0048] The database for the system diagnostic platform was redesigned, employing both relational and non-relational databases. Relational databases use a relational model to organize data, storing it in rows and columns. They store data in a structured manner, requiring tables to have a predefined structure before data is stored. Because the data format and content are defined before storage, the reliability and stability of the entire database table are relatively high. Popular relational databases include Oracle and MySQL. In the system diagnostic platform, a relational database is used to store the code for business layer scripts and programs, along with version information, facilitating simple commits and rollbacks (equivalent to backups).

[0049] With the rise of Web 2.0 websites, traditional relational databases have become increasingly inadequate in handling large-scale and high-concurrency dynamic data, encountering numerous insurmountable problems. Non-relational databases emerged to address the challenges posed by large-scale datasets with diverse data types. A major advantage of non-relational databases is that they no longer focus on the relational characteristics of traditional databases; data is independent of each other, thus enhancing scalability at the architectural level. This system employs a document-oriented database (MongoDB) to store semi-structured document data, including component information, real-time status, and various logs generated during operation of the machine under test, for subsequent fault analysis.

[0050] S3: Modify the deployment method of business layer program code.

[0051] This step includes: decoupling the functions of the system layer and the business layer; after decoupling, storing the source code of the system layer in files on a central server; developing a management module on top of the system layer, introducing an online code editor framework, and using this editor to develop, check, and test operation and maintenance scripts; storing the code of the version control function components in binary form into a new database system; extracting diagnostic rules and indicators, stress test thresholds, and system log blacklists and whitelists, adjusting the extracted indicator data based on research into server production diagnostics, and storing it in the management module.

[0052] As an example, after decoupling the system layer and business layer functions, the source code of the system layer functions remains unchanged and is stored as files on a central server. A management module is developed above the system layer, introducing an online code editor framework. This editor is used to develop, syntax-check, and test operation and maintenance scripts. It also has basic version control functions (commit and rollback). After development, the code is stored in binary form in the database. Additionally, diagnostic rules and metrics, stress test thresholds, and system log blacklists and whitelists need to be extracted. Through research on server production diagnostics, it was found that adjusting these metric data constitutes a significant portion of the operation and maintenance work during production. Using a management module to store this data allows for rapid maintenance and adjustments, and the adjustments take effect immediately without requiring modifications to the source code for release, thus improving operational efficiency and reducing risks.

[0053] S4: Collect the operating data of the machine under test through the whole system diagnostic platform, and input it into the trained diagnostic model for fault diagnosis.

[0054] As an example, the system diagnostic platform collects the operating data of the machine under test. The operating data includes the configuration information of components during the production process, firmware version information, performance indicators, stress test data, generated system logs and BMC SEL logs. The operating data is used as the operating features for fault diagnosis and input into the trained diagnostic model to perform fault diagnosis, and the diagnostic results are output. The diagnostic results are collected and classified, and the failure rate of each configuration model in each stage is calculated.

[0055] Specifically, the system diagnostic platform collects operational data from the machine under test, including configuration information of various components during production, firmware version information, performance indicators, stress test data, generated system logs, and BMCSEL logs. This data serves as operational features for fault diagnosis and is input into a pre-trained diagnostic model. The diagnostic model, trained automatically using a machine learning framework based on extensive operational data, fault information, and diagnostic results, is the most accurate target model for fault diagnosis. Based on this model, corresponding diagnostic results are predicted. When components of the machine under test undergo iterative upgrades, the platform allows for simple and quick maintenance, adjusting relevant performance indicators and stress thresholds for more accurate system-wide diagnostics.

[0056] In addition, during the production process, various faults that occur in the production of the machines to be tested are collected and classified. The failure rate of individual faults occurring in each stage of different machine models and configurations is statistically analyzed to provide standardized guidance for safe production on the production line.

[0057] This embodiment provides an optimization method for a whole-system diagnostic platform. Based on traditional system diagnostic platforms, it abstracts various diagnostic functions, performance indicators, stress thresholds, and fault blacklists / whitelists, storing them in binary form in the platform database. Maintenance is performed through a separate web function, offering flexible maintenance and deployment, immediate effectiveness, and independence from or impact on overall system upgrades and iterations. Fault information and system logs are collected and analyzed during production to provide standardized guidance for safe production. This method reduces the frequency of hotfixes on the whole-system diagnostic platform, enhances platform maintainability, improves operational efficiency, and reduces operational risks.

[0058] Example 2:

[0059] Based on Example 1, such as Figure 2 As shown, the present invention also discloses an optimization system for a whole system diagnostic platform, including: a function splitting module 1, a database construction module 2, a deployment module 3, and a fault diagnosis module 4.

[0060] Functional splitting module 1 is used to split the whole system diagnostic platform into a system layer and a business layer.

[0061] Database building module 2 is used to build a new database system for the whole system diagnostic platform. The new database system includes relational databases and non-relational databases.

[0062] Deployment Module 3 is used to modify the deployment method of business layer program code.

[0063] Fault diagnosis module 4 is used to collect the operating data of the machine under test through the whole system diagnostic platform and input it into the trained diagnostic model for fault diagnosis.

[0064] This embodiment provides an optimized system for a whole system diagnostic platform, which can reduce the frequency of hotfixes on the whole system diagnostic platform, enhance the platform's maintainability, improve operational efficiency, and reduce operational risks.

[0065] Example 3:

[0066] This embodiment discloses an optimization device for a whole system diagnostic platform, including a processor and a memory; wherein, when the processor executes the optimization program for the whole system diagnostic platform stored in the memory, it performs the following steps:

[0067] 1. The whole system diagnostic platform is split into a system layer and a business layer.

[0068] 2. Build a new database system for the whole system diagnostic platform. The new database system includes relational databases and non-relational databases.

[0069] 3. Modify the deployment method of the business layer program code.

[0070] 4. Collect the operating data of the machine under test through the whole system diagnostic platform, and input it into the trained diagnostic model for fault diagnosis.

[0071] Furthermore, the optimization device for the whole system diagnostic platform in this embodiment may further include:

[0072] The input interface is used to acquire the optimized program of the external system diagnostic platform and save it to the memory. It can also acquire various instructions and parameters transmitted from external terminal devices and transmit them to the processor, so that the processor can perform corresponding processing using these instructions and parameters. In this embodiment, the input interface may specifically include, but is not limited to, a USB interface, a serial interface, a voice input interface, a fingerprint input interface, and a hard disk read interface.

[0073] An output interface is used to output various data generated by the processor to connected terminal devices, so that other terminal devices connected to the output interface can obtain the various data generated by the processor. In this embodiment, the output interface may include, but is not limited to, a USB interface, a serial interface, etc.

[0074] A communication unit is used to establish a remote communication connection between the optimization device of the whole system diagnostic platform and an external server, so that the optimization device of the whole system diagnostic platform can mount the image file to the external server. In this embodiment, the communication unit may specifically include, but is not limited to, a remote communication unit based on wireless communication technology or wired communication technology.

[0075] The keyboard is used to acquire various parameter data or commands input by the user through real-time keystrokes.

[0076] The monitor is used to display relevant information in real time regarding the process of locating a short circuit in the server's power supply line.

[0077] A mouse can be used to assist users in inputting data and simplifying user operations.

[0078] This embodiment also discloses a readable storage medium, which includes random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium known in the art. The readable storage medium stores an optimization program for a system diagnostic platform, which, when executed by a processor, performs the following steps:

[0079] 1. The whole system diagnostic platform is split into a system layer and a business layer.

[0080] 2. Build a new database system for the whole system diagnostic platform. The new database system includes relational databases and non-relational databases.

[0081] 3. Modify the deployment method of the business layer program code.

[0082] 4. Collect the operating data of the machine under test through the whole system diagnostic platform, and input it into the trained diagnostic model for fault diagnosis.

[0083] In summary, this invention can significantly reduce the frequency of program deployments caused by hotfixes, thereby reducing the workload of operations and maintenance and the risk of production anomalies.

[0084] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0085] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0086] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0087] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0089] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0090] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0091] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0092] The optimization method, system, device, and readable storage medium of the whole system diagnostic platform provided by this invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. An optimization method for a whole system diagnostic platform, characterized in that, The method comprises the following steps: splitting the whole machine system diagnosis platform into a system layer and a business layer; constructing a new database system for the whole machine system diagnosis platform, wherein the new database system comprises a relational database and a non-relational database; transforming the deployment form of the business layer program code; collecting running data of a machine to be detected through the whole machine system diagnosis platform, and inputting a trained diagnosis model to perform fault diagnosis; the transformed deployment form of the business layer program code comprises: functionally decoupling the system layer and the business layer; after decoupling, storing the source code of the system layer in a central server in the form of a file; developing a management module above the system layer, introducing an online code editor framework, and developing, syntax checking and testing an operation and maintenance script through the editor; storing the code of a version control function component in the new database system in the form of binary; extracting diagnosis rules and indexes, threshold values of stress tests and black and white lists of system logs, adjusting the extracted index data through research on server production diagnosis, and storing the adjusted data in the management module.

2. The method of claim 1, wherein: The method of splitting the whole machine system diagnosis platform into the system layer and the business layer comprises: setting the system layer in the whole machine system diagnosis platform, wherein the system layer is used for executing and monitoring a customized script deployed by an operation and maintenance personnel, monitoring a system diagnosis process, collecting basic information of a server preset component in a server detection process, and feeding back a running state of the server in real time, and collecting system logs.

3. The method of claim 2, wherein: The method of splitting the whole machine system diagnosis platform into the system layer and the business layer further comprises: solidifying the source code of the system layer into a group of solidified programs which are stored in the central server and are controlled and managed through a version control tool and are periodically maintained and upgraded.

4. The method of claim 1, wherein: The method of splitting the whole machine system diagnosis platform into the system layer and the business layer comprises: setting the business layer in the whole machine system diagnosis platform, wherein the business layer adopts a diagnosis and check program running above the system layer, and is used for acquiring component configuration and firmware version information, measuring component sensor information indexes, performing component stress tests, and troubleshooting system and component logs.

5. The optimization method of the whole machine system diagnosis platform according to claim 1, wherein: the relational database adopts Oracle or Mysql, is used for saving the code of the business layer script and program, and saves version information of the code; the non-relational database adopts a document type database MongoDB, and is used for storing component information, real-time states and document data generated in a running process of a machine to be detected.

6. The method of claim 1, wherein: The method of collecting running data of a machine to be detected through the whole machine system diagnosis platform, and inputting a trained diagnosis model to perform fault diagnosis comprises: collecting running data of a machine to be detected through the whole machine system diagnosis platform, wherein the running data comprises configuration information, firmware version information, performance indexes, stress test data, generated system logs and BMC SEL logs of a component in a production process; inputting the running data as running features of fault diagnosis into the trained diagnosis model to perform fault diagnosis, and outputting a diagnosis result; collecting the diagnosis result, classifying the diagnosis result, and calculating a fault rate of a single fault of each configuration type in each link.

7. An optimization system for a platform of system diagnostics of a whole machine, characterized in that, The method comprises the following steps: The function splitting module is used for splitting the whole machine system diagnosis platform into a system layer and a business layer. The database construction module is used for constructing a new database system for the whole machine system diagnosis platform, and the new database system comprises a relational database and a non-relational database. The deployment module is used for modifying the deployment form of the business layer program code. The fault diagnosis module is used for collecting operation data of a machine to be detected by the whole machine system diagnosis platform, and inputting the trained diagnosis model to perform fault diagnosis. The modified deployment form of the business layer program code comprises: The system layer and the business layer are functionally decoupled. After the decoupling, the source code of the system layer is stored in a central server in the form of a file. A management module is developed above the system layer, an online code editor framework is introduced, and the development, syntax checking and testing of operation scripts are performed through the editor. The code of a version control function component is stored in the new database system in the form of binary. Diagnostic rules and indexes, threshold values of stress tests and black and white lists of system logs are extracted, the extracted index data is adjusted through research on server production diagnosis, and the adjusted index data is stored in the management module.

8. An optimization apparatus of a whole system diagnosis platform, characterized in that, The memory is used for storing the optimization program of the whole machine system diagnosis platform. The processor is used for performing the optimization program of the whole machine system diagnosis platform to realize the steps of the optimization method of the whole machine system diagnosis platform according to any one of claims 1 to 6. The readable storage medium stores the optimization program of the whole machine system diagnosis platform, and the optimization program of the whole machine system diagnosis platform is executed by the processor to realize the steps of the optimization method of the whole machine system diagnosis platform according to any one of claims 1 to 6.

9. A readable storage medium characterized by: ​

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