Software evaluation method and system oriented to reliability of calculation and storage integrated system

Abstract

The invention provides a calculation and storage integrated system reliability-oriented software evaluation method and system. The method comprises the steps of input analysis, user constraint setting, edge scene selection, simulation testing, monitoring and self-configuration, result output and the like. According to the method, a system design scheme input by a user is analyzed, hardware components and connections thereof are identified, a model is adjusted according to physical conditions set by the user, and a specific application scene is selected for simulation. The simulation test comprises loss, fault and limit condition tests, system state monitoring and automatic optimization configuration. And finally outputting test records, quantitative indexes and scores, and comprehensively evaluating the reliability of the system. The software evaluation platform constructed on the basis of the method is suitable for diversified integrated systems, and accuracy and high efficiency of evaluation are ensured.

Description

Technical Field

[0001] The present invention relates to a reliability evaluation technology for a computing and storage integrated system, and in particular to a software evaluation method and system for the reliability of a computing and storage integrated system. Background Art

[0002] At present, there is a lack of research programs on integrated system reliability assessment methods, and comprehensive simulation prediction through software technology is even blank in the industry. R&D personnel use two methods to analyze the reliability of hardware systems: one is to calculate the reliability index of the system based on the series and parallel relationship of system components using existing mathematical models and theoretical formulas; the other is to split the system into independent components, and use finite element analysis, reliability physical modeling and other technologies for each single component, as well as accelerated life tests to predict quantitative reliability. Both of these technical ideas have obvious defects: the results obtained by the former through theory are often far from the actual application scenarios, and as the system scale becomes larger and the integration increases, the results obtained by theoretical calculations are even less convincing; the latter does not take into account the interaction and constraint relationship between components during analysis, and it is difficult to integrate the reliability indicators of different components into the reliability indicators of the entire system.

[0003] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0004] The main purpose of the present invention is to solve the problems existing in the above-mentioned background technology and to provide a software evaluation method and system for the reliability of a computing and storage integrated system.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect of the present invention, a software evaluation method for reliability of a computing and storage integrated system comprises:

[0007] Input parsing: Receive and parse user input for computing and storage system design, including identifying hardware component models and their connections;

[0008] User constraint setting: adjust the system model to suit different application requirements based on the physical boundary conditions set by the user;

[0009] Edge scenario selection: Provides a library of multiple application scenarios, allowing users to select specific scenarios to simulate system performance in different environments;

[0010] Simulation test: Conduct wear test, fault test and extreme stress test on the adjusted system model to simulate the performance and reliability of the system under various conditions;

[0011] Monitoring and self-configuration: Monitor system status in real time during the test process, and automatically optimize system configuration based on system performance and preset strategies to improve system reliability and performance;

[0012] Result output: Generate a file containing test records, and calculate and provide the system's quantitative indicators and comprehensive scores for users to evaluate the reliability of the system.

[0013] In a second aspect of the present invention, a software evaluation system for reliability of a computing and storage integrated system includes:

[0014] An input parsing module is used to parse the computing and storage system design scheme input by the user and identify the hardware component models and their connection relationships;

[0015] User constraint setting module, used to adjust the system model according to the physical boundary conditions set by the user, such as area, accuracy, temperature and external force;

[0016] The edge scenario selection module is used to provide a library of multiple application scenarios, allowing users to select specific scenarios to meet different application requirements;

[0017] The simulation test module is used to perform wear and tear tests, fault tests and extreme stress tests on the system model to simulate the performance of the system under different conditions;

[0018] Monitoring and self-configuration mechanism module, used to record system status during testing and optimize system configuration according to real-time changes;

[0019] The result output module is used to generate test record files and provide quantitative indicators and comprehensive scores of the system.

[0020] The present invention has the following beneficial effects:

[0021] The present invention is aimed at an integrated system with computing and storage functions, and proposes a software evaluation method and system for the reliability of a computing and storage integrated system. The software evaluation platform constructed based on the present invention is suitable for integrated systems of different scales, different integration levels, and different application scenarios, and has good accuracy, versatility, scalability, and operability.

[0022] The main advantages of the present invention are:

[0023] 1. Provide users with a variety of functional and effective packaging software models for storage and computing hardware, and users can map the connection relationship to the software model according to the actual design.

[0024] 2. Scalability: Provide users with extended compatible design options. Users can design software models of hardware according to their own needs, and can interact with the model provided by the software evaluation platform of the present invention for design and joint simulation.

[0025] 3. Provide setting options of different precision and different physical parameters (such as temperature, external force, etc.). Users can perform simulation predictions on the software evaluation platform based on actual costs, precision requirements, and the environment in which the equipment is located, providing users with simulation results that are highly consistent with actual scenarios.

[0026] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a schematic diagram of the overall process of the software evaluation method for reliability of a computing and storage integrated system according to an embodiment of the present invention.

[0028] Figure 2 This is a flow chart of the preparation phase of the software evaluation method according to an embodiment of the present invention.

[0029] Figure 3 The following is an architecture diagram of a software evaluation system for reliability of a computing and storage integrated system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0031] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0032] See also Figures 1 to 3 The embodiment of the present invention provides a software evaluation method for reliability of a computing and storage integrated system, comprising:

[0033] Input parsing: receiving and parsing the computing and storage system design scheme input by the user, including identifying the hardware component models and their connection relationships; preferably, supporting both text and schematic input;

[0034] User constraint settings: adjust the system model to suit different application requirements based on the physical boundary conditions set by the user, such as area, accuracy, temperature, and external force;

[0035] Edge scenario selection: Provides a library of multiple application scenarios, allowing users to select specific scenarios to simulate system performance in different environments;

[0036] Simulation test: Conduct wear test, fault test and extreme stress test on the adjusted system model to simulate the performance and reliability of the system under various conditions;

[0037] Monitoring and self-configuration: Monitor system status in real time during the test process, and automatically optimize system configuration based on system performance and preset strategies to improve system reliability and performance;

[0038] Result output: Generate a file containing test records, and calculate and provide the system's quantitative indicators and comprehensive scores for users to evaluate the reliability of the system.

[0039] In some embodiments, the input parsing specifically includes: determining the hardware component model and interface information used according to the description of the input file; determining the connection order between different components according to the description of the input file, establishing an interface matching relationship, and generating a corresponding interconnection model; generating an achievable physical layout solution based on the hardware model and the interconnection model.

[0040] In some embodiments, for user constraint settings, the accuracy options are divided into three types: "high accuracy", "medium accuracy" and "low accuracy", which are used to adjust the coarseness of the damage description granularity and the number of benchmark tests during the test algorithm running phase. The higher the accuracy, the longer the time required for evaluation and analysis, and the more accurate the prediction of model reliability.

[0041] In some embodiments, the edge scenarios may include servers, smart homes, underwater operations, automotive electronics, factory production, etc.; wherein, the library model of the edge scenarios is pre-established by pre-collecting data such as throughput, utilization, fault recovery time, loss, etc. of computing and storage devices in the corresponding scenarios during use, combining data analysis and mathematical modeling methods, and includes characteristic curves, key parameters, time series distribution models, etc.

[0042] In some embodiments, the edge scenario selection specifically includes: selecting and retrieving a general library model corresponding to the edge scenario selected by the user from a preset application scenario library; based on the retrieved general library model, adjusting the characteristic parameters of the system model according to the specific needs and characteristics of the system to be evaluated, so that the model can accurately reflect the behavior of the system to be evaluated in a specific edge scenario; based on a time series model of the state change of the library model after a fault interruption, a time series model of the system model to be evaluated after a fault occurs is established in combination with a Markov chain, which is used to predict the recovery process and performance changes of the system under different fault conditions.

[0043] For the simulation test, in some embodiments, the wear test specifically includes: setting wear characteristic parameters in the realizable physical layout model scheme generated in the preparation stage to simulate the wear and aging process of the system in long-term use; deploying predefined benchmark test tasks to the model scheme to evaluate the performance of the system under standard workload; continuously monitoring the wear of the model during the running of the benchmark test tasks, and collecting data to predict the life and performance degradation trend of the system. In some embodiments, the fault test specifically includes: constructing a fault model of the system to be tested according to the constraint parameters set by the user and the library model of the selected scenario, including defining the fault damage characteristics and predicting the fault recovery time; deploying the benchmark test tasks on the model scheme to ensure the consistency and comparability of the test environment; during the running of the benchmark test tasks, randomly introducing preset fault programs to different components of the system to simulate various types of fault conditions that may occur in the real environment; evaluating the functional performance and recovery ability of the system after being damaged by the fault, and analyzing the fault tolerance and robustness of the system. In some embodiments, the extreme forced test specifically includes: determining the extreme values ​​of the system in performance and physical environment, such as throughput, bandwidth, temperature, and stress, based on model characteristics and user constraints; deploying benchmark test tasks on the model scheme; adjusting the relevant parameters representing the performance and physical environment in the model to the extreme values ​​to simulate the performance of the system under extreme working conditions; generating time series information of the damage status of the test model under extreme conditions based on the library model of the edge scenario, which is used to analyze the stability and reliability of the system under extreme conditions; observing and evaluating the functionality of the system under extreme conditions.

[0044] In some embodiments, the monitoring and self-configuration specifically include: deploying the benchmark task on the model scheme, and the platform records status information, including key execution path information, system model real-time performance information, system model feature information, etc.; searching for the configuration scheme of the benchmark task on the system model, with extending life and ensuring functional correctness as optimization goals, adjusting configuration indicators such as parallelism, utilization, and bandwidth; establishing a configuration scheme state transition tensor graph with the key execution path as the core based on the adjustment of real-time parameter indicators; generating a configuration scheme state transfer tensor graph at the end of each benchmark task execution, and multiple configuration scheme state transfer tensor graphs can be obtained after a large number of rounds of benchmark task execution; and obtaining the reliability indicator for the optimal configuration based on multiple configuration scheme state transfer tensor graphs.

[0045] See also Figure 3The embodiment of the present invention also provides a software evaluation system for the reliability of a computing and storage integrated system, including: an input parsing module, which is used to parse the computing and storage system design scheme input by the user, and identify the hardware component model and its connection relationship; generally, it supports two input forms: text and schematic diagram; a user constraint setting module, which is used to adjust the system model according to the physical boundary conditions set by the user, such as area, accuracy, temperature and external force; an edge scenario selection module, which is used to provide a variety of application scenario libraries, allowing users to select specific scenarios to meet different application requirements; a simulation test module, which is used to perform wear tests, fault tests and extreme forced tests on the system model to simulate the performance of the system under different conditions; a monitoring and self-configuration mechanism module, which is used to record the system status during the test and optimize the system configuration according to real-time changes; a result output module, which is used to generate a test record file and provide quantitative indicators and comprehensive scores for the system.

[0046] The present invention provides a comprehensive software evaluation method and system, which specifically analyzes and predicts the reliability of computing and storage integrated systems. The method overcomes the limitations of the prior art that relies on theoretical calculations or single component analysis by integrating simulation software models and test algorithms of multiple hardware. It can simulate the actual performance of the system in different application scenarios, including wear, failure and extreme condition tests, so as to more accurately predict the reliability of the system in actual use. In addition, the software platform constructed based on the present invention supports users to set physical boundary conditions such as area, accuracy, temperature and external force according to specific needs to achieve highly customized simulation tests. The platform also has high scalability and operability, allowing users to design and integrate their own hardware models, and interactively design and jointly simulate with the models provided by the platform. By providing setting options of different accuracy and physical parameters, the present invention can provide users with simulation results that meet the actual application environment, thereby optimizing system performance in the design stage and improving overall reliability and efficiency.

[0047] The specific embodiments of the present invention are further described below.

[0048] According to the software evaluation method for the reliability of computing and storage integrated systems of the present invention, an integrated software evaluation platform is designed. The platform has built-in simulation software models for a variety of hardware, including a variety of processors, SRAM, DRAM, cache, DMA, etc.; it also integrates a variety of test algorithms, which can perform reliability evaluation and prediction on the designed computing and storage systems in terms of service life, handling of unexpected faults, and operation conditions in extreme physical environments. The software evaluation platform supports two input forms: text and schematic diagrams. After the user inputs the design scheme to be evaluated, the software platform will automatically parse the input file. After the user's constraint setting is completed, the platform enters the simulation execution stage, calls the test algorithm to run on the parsed input model, and after the execution process ends, it will output the reliability result file for the user to view. The workflow of the software evaluation platform is as follows Figure 1 The system architecture of the software evaluation platform is shown in Figure 3 shown.

[0049] The software platform parses the input file as follows: (1) Determine the hardware component model and interface information to be used based on the description of the input file; (2) Determine the connection sequence between different components based on the description of the input file, establish interface matching relationships, and generate the corresponding interconnection model; (3) Generate an implementable physical layout solution based on the hardware model and interconnection model.

[0050] The constraints that users can set include area, accuracy, temperature, external force, etc. The software platform can adjust the physical layout plan again according to the user's settings. If it cannot be physically realized under the user's constraint settings, the user will be reminded to adjust the constraint settings. The setting range of constraint indicators such as area and temperature is the boundary condition of the simulation test. The accuracy options are divided into three types: "high accuracy", "medium accuracy" and "low accuracy", which are used to adjust the coarseness of the damage description granularity, the number of benchmark tests, etc. in the subsequent test algorithm running stage. The higher the accuracy, the longer the time required for evaluation and analysis, and the more accurate the prediction of model reliability. The input parsing and constraint setting process are collectively referred to as the "preparation stage", such as Figure 2 shown.

[0051] In addition to adding constraints on physical boundary parameters such as area and temperature, users can also choose a variety of edge scenarios provided by our platform as application areas for product prediction. The edge scenarios provided by the platform include servers, smart homes, underwater operations, automotive electronics, factory production, etc. We tracked and collected data such as throughput, utilization, fault recovery time, and loss of computing and storage devices in the above scenarios during use, and combined data analysis and mathematical modeling methods to establish a library model for the corresponding scenario, including characteristic curves, key parameters, and time series distribution models. After the user selects an edge scenario for the system to be evaluated, the platform will perform the following steps: (1) Retrieve the general library model of the edge scenario; (2) Use the general library model as a template to adjust the characteristic parameters of the system model to be evaluated; (3) Based on the time series model of the library model's state change after a fault interruption, combine the Markov chain to establish a time series model of the system model to be evaluated after the fault.

[0052] The evaluation process for the model mainly includes wear test, fault test and extreme stress test, and the system is simulated and tested under normal conditions, when faults occur and under extreme conditions. The wear test process is as follows: (1) The platform sets the wear characteristic parameters for the realizable physical layout model scheme generated in the "preparation phase"; (2) The benchmark test task is deployed on the model scheme; (3) The wear of the model is observed during the operation of the task to obtain the predicted data of the life. The fault test process is as follows: (1) According to the constraint parameters and the library model of the scenario, a fault damage model of the system to be tested is established, including fault damage characteristics and recovery time prediction; (2) The benchmark test task is deployed on the model scheme; (3) Fault programs are randomly introduced into different components of the system during the operation of the benchmark test task; (4) The functionality of the model after being damaged by the fault is observed. Extreme stress testing: (1) Determine the system's extreme values ​​in performance and physical environment based on the model and constraints, such as throughput, bandwidth, temperature, stress, etc.; (2) Deploy the benchmark test task on the model solution; (3) Adjust the relevant parameters in the model that represent performance and physical environment to extreme values; (4) Generate time series information on the damage status of the test model under extreme conditions based on the library model of the edge scenario; (5) Observe the functionality of the model under extreme conditions.

[0053] During the test phase, monitoring and self-configuration mechanisms are introduced: the monitoring mechanism can record various states of the system model during the test, such as failure, recovery, and throughput; the self-configuration mechanism is used to continuously monitor the system's behavior in the execution of benchmark tasks according to the real-time changes of the model during the test, so as to observe the upper and lower limits of the reliability of the model in the test. The execution process of the monitoring and self-configuration mechanism is as follows: (1) The benchmark task is deployed on the model scheme, and the platform records the status information, including key execution path information, system model real-time performance information, system model feature information, etc.; (2) Search for the configuration scheme of the benchmark task on the system model, and adjust the configuration indicators such as parallelism, utilization, and bandwidth with the optimization goal of extending the life and ensuring functional correctness; (3) According to the adjustment of real-time parameter indicators, a configuration scheme state transition tensor graph with the key execution path as the core is established; (4) A configuration scheme state transition tensor graph is generated at the end of each benchmark task execution. After a large number of rounds of benchmark task execution, multiple configuration scheme state transition tensor graphs can be obtained; (5) The reliability index of the optimal configuration is obtained based on the multiple configuration scheme state transition tensor graphs.

[0054] After the test is completed, the evaluation platform will output record files, quantitative indicators and comprehensive scores to provide users with comprehensive reliability analysis data.

[0055] In summary, compared with the existing technologies that have defects in integrated system reliability assessment, such as relying on theoretical calculations or only analyzing single components, these methods cannot accurately reflect the system reliability in actual application scenarios. The present invention provides a more comprehensive assessment method that is closer to actual application scenarios, thereby overcoming the limitations of the existing technologies. The software assessment platform constructed based on the present invention is suitable for integrated systems of different scales, different integration levels, and different application scenarios, and has good accuracy, versatility, scalability, and operability.

[0056] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0057] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0058] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0059] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0060] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0061] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0062] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0063] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0064] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0065] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0066] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0067] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0068] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A software evaluation method for reliability of computing and storage integrated systems, characterized in that: include: Input parsing: Receive and parse user input for computing and storage system design, including identifying hardware component models and their connections; User constraint setting: adjust the system model to suit different application requirements based on the physical boundary conditions set by the user; Edge scenario selection: Provides a library of multiple application scenarios, allowing users to select specific scenarios to simulate system performance in different environments; Simulation test: Conduct wear test, fault test and extreme stress test on the adjusted system model to simulate the performance and reliability of the system under various conditions; Monitoring and self-configuration: Monitor system status in real time during the test process, and automatically optimize system configuration based on system performance and preset strategies to improve system reliability and performance; Result output: Generate a file containing test records, and calculate and provide the system's quantitative indicators and comprehensive scores for users to evaluate the reliability of the system.

2. The software evaluation method for computing and storage integrated system reliability according to claim 1, characterized in that: The input parsing specifically includes: Determine the hardware component model and interface information to be used according to the description of the input file; Determine the connection order between different components according to the description of the input file, establish interface matching relationships, and generate corresponding interconnection models; Generate an achievable physical layout solution based on the hardware model and interconnect model.

3. The software evaluation method for computing and storage integrated system reliability according to claim 1 or 2, characterized in that: The accuracy options are divided into three types: "high accuracy", "medium accuracy" and "low accuracy", which are used to adjust the granularity of damage description and the number of benchmark tests during the test algorithm operation phase. The higher the accuracy, the longer the evaluation and analysis time required, and the more accurate the prediction of model reliability.

4. The software evaluation method for computing and storage integrated system reliability according to any one of claims 1 to 3, characterized in that: The edge scenarios include servers, smart homes, underwater operations, automotive electronics, and factory production; wherein, the library model of the edge scenarios is pre-established by pre-collecting the throughput, utilization, fault recovery time, and loss data of the computing and storage devices in the corresponding scenarios during use, combining data analysis and mathematical modeling methods, and includes characteristic curves, key parameters, time series distribution models, etc.

5. The software evaluation method for computing and storage integrated system reliability according to any one of claims 1 to 4, characterized in that: The edge scenario selection specifically includes: Select and call a general library model corresponding to the edge scenario selected by the user from the preset application scenario library; Based on the retrieved general library model, adjust the characteristic parameters of the system model according to the specific needs and characteristics of the system to be evaluated, so that the model can accurately reflect the behavior of the system to be evaluated in a specific edge scenario; According to the time series model of the state change of the library model after the fault interruption, the time series model of the system model to be evaluated after the fault occurs is established in combination with the Markov chain, which is used to predict the recovery process and performance changes of the system under different fault conditions.

6. The software evaluation method for computing and storage integrated system reliability according to any one of claims 1 to 5, characterized in that: The wear test specifically includes: In the realizable physical layout model solution generated in the preparation stage, the loss characteristic parameters are set to simulate the wear and aging process of the system in long-term use; Deploy predefined benchmark tasks to the model solution to evaluate the performance of the system under standard workloads; Continuously monitor the wear and tear of the model while running benchmark tasks, and collect data to predict the system's lifespan and performance degradation trends.

7. The software evaluation method for computing and storage integrated system reliability according to any one of claims 1 to 6, characterized in that: The fault test specifically includes: Construct a fault model of the system to be tested based on the constraint parameters set by the user and the library model of the selected scenario, including defining the fault damage characteristics and predicting the fault recovery time; Deploy benchmark tasks on the model solution to ensure the consistency and comparability of the test environment; During the benchmark task execution, preset fault programs are randomly introduced into different components of the system to simulate various fault conditions that may occur in the real environment; Evaluate the system's functional performance and recovery capabilities after a fault, and analyze the system's fault tolerance and robustness.

8. The software evaluation method for computing and storage integrated system reliability according to any one of claims 1 to 7, characterized in that: The extreme forced test specifically includes: Determine the system's performance and physical environment limits based on model characteristics and user constraints; Deploy benchmark tasks on the model solution; Adjust the relevant parameters in the model that represent performance and physical environment to extreme values ​​to simulate the performance of the system under extreme working conditions; Based on the library model of edge scenarios, the time series information of the damage status of the test model under extreme conditions is generated to analyze the stability and reliability of the system under extreme conditions; Observe and evaluate the functionality of the system under extreme conditions.

9. The software evaluation method for computing and storage integrated system reliability according to any one of claims 1 to 8, characterized in that: The monitoring and self-configuration specifically include: Deploy the benchmark task on the model solution and record the status information, including key execution path information, system model real-time performance information, and system model feature information; Search for the configuration scheme of the benchmark task on the system model, adjust the configuration indicators with the optimization goal of extending the life and ensuring the functional correctness; According to the adjustment of real-time parameter indicators, a configuration scheme state transition tensor graph with the key execution path as the core is established; Each time a benchmark task is executed, a configuration scheme state transition tensor graph is generated. After a large number of rounds of benchmark task execution, multiple configuration scheme state transition tensor graphs can be obtained. The reliability index of the optimal configuration is obtained by synthesizing the state transition tensor graphs of multiple configuration schemes.

10. A software evaluation system for reliability of computing and storage integrated systems, characterized in that: include: An input parsing module is used to parse the computing and storage system design scheme input by the user, identify the hardware component models and their connection relationships; preferably, it supports both text and schematic input forms; A user constraint setting module is used to adjust the system model according to the physical boundary conditions set by the user; The edge scenario selection module is used to provide a library of multiple application scenarios, allowing users to select specific scenarios to meet different application requirements; The simulation test module is used to perform wear and tear tests, fault tests and extreme stress tests on the system model to simulate the performance of the system under different conditions; Monitoring and self-configuration mechanism module, used to record system status during testing and optimize system configuration according to real-time changes; The result output module is used to generate test record files and provide quantitative indicators and comprehensive scores of the system.

Images