System Performance Evaluation Method, Device, Computer Equipment and Storage Medium

Abstract

The present application relates to the technical field of performance analysis, and particularly to a system performance evaluation method, device, computer device, and storage medium. The method includes: constructing a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated; predicting initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model; and evaluating the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task to obtain the target performance parameters of the system to be evaluated. The present application improves the evaluation accuracy of the system performance of the system to be evaluated, and ensures that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, so as to accurately obtain the target performance parameters of the system to be evaluated.

Description

Technical Field

[0001] This application relates to the technical field of performance analysis, and particularly to a system performance evaluation method, device, computer device, and storage medium. Background Art

[0002] With the continuous development of network technologies, more and more network systems have been established and put into use. To perform targeted management and operation and maintenance on different network systems, it is necessary to evaluate the system performance of each network system to achieve operation and maintenance management of the network system based on the system performance.

[0003] However, the prior art cannot achieve the system performance evaluation of network systems. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a system performance evaluation method, device, computer device, and storage medium that can accurately evaluate the system performance of network systems.

[0005] In a first aspect, this application provides a system performance evaluation method. The method includes:

[0006] Construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0007] Predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model;

[0008] Evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task to obtain the target performance parameters of the system to be evaluated.

[0009] In one embodiment, the constructing a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated includes:

[0010] Construct an initial system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0011] Convert the initial system model into the Petri net form to obtain the target system model in the Petri net form.

[0012] In one embodiment, the internal structure information includes system interface information and system hierarchy information; the external environment information includes system scenario information and operating environment information; the constructing an initial system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated includes:

[0013] Construct a first internal model of the system to be evaluated according to the system interface information;

[0014] Construct a second internal model of the system to be evaluated according to the system hierarchy information;

[0015] Construct a first external model of the system to be evaluated according to the system scenario information;

[0016] Construct a second external model of the system to be evaluated according to the operating environment information;

[0017] Integrate the first internal model, the second internal model, the first external model, and the second external model to obtain an initial system model corresponding to the system to be evaluated.

[0018] In one embodiment, the constructing a second internal model of the system to be evaluated according to the system hierarchy information includes:

[0019] Construct a system area sub-model by constructing a model for the system partitions included in the system to be evaluated according to the system hierarchy information;

[0020] Construct a regional task sub-model by constructing a model for the regional tasks included in each system partition of the system to be evaluated according to the system hierarchy information;

[0021] Integrate the system area sub-model and the regional task sub-model to obtain a second internal model of the system to be evaluated.

[0022] In one embodiment, the evaluating the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task to obtain the target performance parameters of the system to be evaluated includes:

[0023] Construct a state transition probability matrix corresponding to each system task according to the initial performance parameters corresponding to each system task;

[0024] Evaluate the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters to obtain the target performance parameters of the system to be evaluated.

[0025] In one embodiment, the initial performance parameters include an initial timeliness parameter and an initial utilization parameter; the evaluating the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters to obtain the target performance parameters of the system to be evaluated includes:

[0026] Perform timeliness evaluation on the system to be evaluated according to the initial timeliness parameters and the state transition probability matrix, and obtain the target timeliness parameter in the target performance parameters;

[0027] Perform utilization evaluation on the system to be evaluated according to the initial utilization parameters and the state transition probability matrix, and obtain the target utilization parameter in the target performance parameters.

[0028] In a second aspect, the present application also provides a system performance evaluation device. The device includes:

[0029] A construction module, configured to construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0030] A prediction module, configured to predict initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model;

[0031] An evaluation module, configured to evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task, and obtain the target performance parameters of the system to be evaluated.

[0032] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0034] Predict initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model;

[0035] Evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task, and obtain the target performance parameters of the system to be evaluated.

[0036] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0038] Predict initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model;

[0039] Evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each of the system tasks, and obtain the target performance parameters of the system to be evaluated.

[0040] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0042] Predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model;

[0043] Evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each of the system tasks, and obtain the target performance parameters of the system to be evaluated.

[0044] The above system performance evaluation method, device, computer device, and storage medium predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated through the target system model corresponding to the system to be evaluated. Furthermore, the system performance of the system to be evaluated is evaluated according to the initial performance parameters corresponding to each system task, and the target performance parameters of the system to be evaluated are obtained. According to the above content, since the target system model corresponding to the system to be evaluated is constructed based on the internal structure information and external environment information of the system to be evaluated, the initial performance parameters corresponding to each system task predicted according to the target system model can accurately reflect the performance of each system task in the system to be evaluated; furthermore, it is ensured that the target performance parameters of the system to be evaluated determined according to the initial performance parameters corresponding to each system task can also accurately reflect the actual situation of the system to be evaluated, improving the evaluation accuracy of the system performance evaluation of the system to be evaluated, and ensuring that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, and the target performance parameters of the system to be evaluated can be accurately obtained. Description of the Drawings

[0045] Figure 1 It is an application environment diagram of a system performance evaluation method provided by an embodiment of the present application;

[0046] Figure 2 It is a flowchart of the first system performance evaluation method provided by an embodiment of the present application;

[0047] Figure 3 It is a flowchart of the second system performance evaluation method provided by an embodiment of the present application;

[0048] Figure 4Schematic diagram of the model processing flow provided by the embodiments of the present application;

[0049] Figure 5 Schematic flow chart of the third system performance evaluation method provided by the embodiments of the present application;

[0050] Figure 6 Schematic flow chart of the fourth system performance evaluation method provided by the embodiments of the present application;

[0051] Figure 7 Block diagram of the structure of the first system performance evaluation device provided by the embodiments of the present application;

[0052] Figure 8 Internal structure diagram of a computer device in one embodiment. Detailed implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. In the description of the present application, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0055] The system performance evaluation method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed on the cloud or other network servers. Through the target system model corresponding to the system to be evaluated, predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated. Furthermore, evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task, and obtain the target performance parameters of the system to be evaluated. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0056] In one embodiment, as Figure 2 shown, a system performance evaluation method is provided. Taking the terminal 102 in Figure 1 as an example, the method includes the following steps:

[0057] S201, construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated.

[0058] It should be noted that to ensure that the constructed target system model can accurately reflect the actual situation of the system to be evaluated, the system characteristics of the system to be evaluated that are different from other network systems can be analyzed, and the system characteristics that are different from other network systems can be used as the simulation modeling angle for constructing the target system model, so that the subsequent constructed target system model can not only effectively reflect the system characteristics of the system to be evaluated, but also make the constructed target system model different from the system models of other network systems.

[0059] In an embodiment of the application, at least two simulation modeling angles for the system to be evaluated are determined in advance. Furthermore, according to the internal structure information and external environment information of the system to be evaluated, model construction is carried out for the system to be evaluated for each simulation modeling angle, and the initial models corresponding to each simulation modeling angle are obtained. Then, the initial models are integrated to obtain the target system model corresponding to the system to be evaluated.

[0060] Furthermore, to ensure that the subsequent operation of predicting the initial performance parameters corresponding to at least two system tasks in the system to be evaluated can be carried out smoothly for the constructed target system model, it is also necessary to perform format conversion on the model format of the target system model to ensure that the initial performance parameters corresponding to at least two system tasks in the system to be evaluated can be predicted smoothly subsequently.

[0061] In an embodiment of the present application, an initial system model corresponding to the system to be evaluated can be constructed in advance according to the internal structure information and external environment information of the system to be evaluated; furthermore, a preset model format is determined, and the initial system model is subjected to format conversion for the model format to obtain a target system model in the model format.

[0062] S202, predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model.

[0063] It should be noted that when it is necessary to predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated, the fault data corresponding to the system to be evaluated can be obtained in advance, and furthermore, the target system model is subjected to simulation operation according to the fault data to implement the operation of predicting the initial performance parameters corresponding to at least two system tasks in the system to be evaluated.

[0064] Among them, the fault data includes data types such as "fault location", "fault cause", "fault description", "fault impact", "fault level", "fault handling method", etc. Further, the fault location is used to describe the location information where the fault occurs in the system; the fault location can include system functions, hardware items, software items, etc.; the fault cause is used to describe the conditions that cause the fault to be triggered; the fault cause can include abnormal input data, incorrect operation behaviors, unclear logic branches, etc.; the fault description is used to describe the fault content and the possible function failure behaviors caused; the fault impact is used to describe the possible impacts on the task completion and operation safety of the system to be evaluated after the fault is triggered; the fault level is used to describe the severity level of the impact of the system fault; the fault level can be divided into levels such as catastrophic, severe, general, negligible according to relevant standards; the fault handling method is used to describe the measures taken by system developers to eliminate or control the fault; the fault handling method can include design improvement, adding protection, standardizing operation behaviors, etc.

[0065] Among them, the initial performance parameters corresponding to the system task can include the initial timeliness parameter and the initial utilization parameter for the system task, as well as the transition probability determined according to the system operation situation when the system to be evaluated runs each system task. The transition probability is the transition probability of the system to be evaluated changing from one state to another when running each system task.

[0066] S203, evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task to obtain the target performance parameters of the system to be evaluated.

[0067] In an embodiment of the present application, when it is necessary to evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task, it is necessary to pre-guarantee that the target system model is isomorphic to Markov. Then, by using the isomorphism between Markov and the target system model, a state transition probability matrix of the system to be evaluated is constructed to evaluate the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters, and the target performance parameters of the system to be evaluated are obtained.

[0068] The above system performance evaluation method predicts the initial performance parameters corresponding to at least two system tasks in the system to be evaluated through the target system model corresponding to the system to be evaluated. Then, the system performance of the system to be evaluated is evaluated according to the initial performance parameters corresponding to each system task, and the target performance parameters of the system to be evaluated are obtained. According to the above content, since the target system model corresponding to the system to be evaluated is constructed based on the internal structure information and external environment information of the system to be evaluated, the initial performance parameters corresponding to each system task predicted according to the target system model can accurately reflect the performance of each system task in the system to be evaluated. Furthermore, it is ensured that the target performance parameters of the system to be evaluated determined according to the initial performance parameters corresponding to each system task can also accurately reflect the actual situation of the system to be evaluated, improving the evaluation accuracy of evaluating the system performance of the system to be evaluated, and ensuring that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, and the target performance parameters of the system to be evaluated can be accurately obtained.

[0069] In one embodiment, as Figure 3 shown, when it is necessary to construct the target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated, the following specific contents may be included:

[0070] S301, construct an initial system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated.

[0071] Specifically, the internal structure information includes system interface information and system hierarchy information; the external environment information includes system scenario information and operating environment information. Therefore, when constructing the initial system model corresponding to the system to be evaluated, the following contents may be included: construct the first internal model of the system to be evaluated according to the system interface information; construct the second internal model of the system to be evaluated according to the system hierarchy information; construct the first external model of the system to be evaluated according to the system scenario information; construct the second external model of the system to be evaluated according to the operating environment information; perform integration processing on the first internal model, the second internal model, the first external model, and the second external model to obtain the initial system model corresponding to the system to be evaluated.

[0072] Among them, the initial system model is a model obtained by constructing a model of the internal structure information and external environment information of the system to be evaluated according to the AADL language.

[0073] In an embodiment of the present application, first, with the help of the AADL language, model the information such as the external input / output interface communication protocol, cross-linked devices, interface data, software and hardware coupling, etc. of the components of the system to be evaluated to obtain the first internal model of the system to be evaluated. Among them, the first internal model can reflect design features such as data interaction and control behavior between the system and external devices and between components. With the help of the AADL language, construct the second internal model of the system to be evaluated for the system-level information. The second internal model can describe information such as the hierarchical subordination between various software and hardware components in the system to be evaluated, as well as information such as the component function control flow in the system to be evaluated. Further, through the second internal model, the design features such as the system component hierarchy, combination, data, control, and interaction can be reflected from static and dynamic perspectives. With the help of the AADL language, construct the first external model of the system to be evaluated for the system scenario information; the first external model can describe various working states or task modes of the system to be evaluated from different angles, clarify the interaction information such as execution conditions and paths between various usage scenarios corresponding to the system to be evaluated. Further, the first external model can comprehensively reflect information such as the usage method and operation profile of the system to be evaluated. With the help of the AADL language, model the information such as the underlying hardware resources, operating system software resources, processing logic of external cross-linked devices, communication protocols, etc. in the operating environment information to obtain the second external model of the system to be evaluated, providing a simulated operating environment for subsequent performance evaluation.

[0074] It should be noted that the system to be evaluated has strict requirements for real-time performance and needs to respond immediately. During the entire process of system operation, if the response of the task set exceeds a certain time, unpredictable consequences will occur. Therefore, an important factor in evaluating real-time performance is the system schedulability, and the system scheduling ability is the key to ensuring real-time performance. To meet the system requirements, the system design will adopt a design method of layer partitioning and inter-layer tasks.

[0075] Therefore, when constructing the second internal model of the system to be evaluated according to the system-level information, it may also include the process of constructing the system area sub-model and the area task sub-model. Furthermore, according to the system area sub-model and the area task sub-model, determine the second internal model; specifically, according to the system-level information, model the system partitions included in the system to be evaluated to obtain the system area sub-model; according to the system-level information, model the area tasks included in each system partition of the system to be evaluated to obtain the area task sub-model; perform an integration process on the system area sub-model and the area task sub-model to obtain the second internal model of the system to be evaluated.

[0076] In an embodiment of the present application, the system to be evaluated uses partitions as the units for scheduling, resource allocation, and protection applications, isolating the applications so that the failure of one partition does not affect other partitions running on the same processor, and thus does not cause the spread of system failures. There are two levels of isolation here: time partitioning and space partitioning respectively.

[0077] Among them, time partitioning: The system periodically schedules each partition repeatedly, and each partition has no priority attribute. Only when the specified time arrives, the operating system will call it so that it can run. A partition is active if and only if, and the others are idle partitions. Therefore, the system can ensure the response time. When a certain partition crashes, the other partitions can continue to run. Space partitioning: Through the storage manager mechanism, it is ensured that each partition has an independent virtual address, so that each partition can have a dedicated space to run its own code.

[0078] Furthermore, the system area sub-model obtained by constructing a model for the system partitions included in the system to be evaluated is composed of three AADL components, including a virtual processor component, a process component, and a memory component. The process component in AADL provides space isolation, meets the requirements of inter-layer partitioning, and represents that the process is bound to a memory. The AADL attribute Actual_Memory_Binding is used here. Similarly, for the process, it is bound to a virtual processor through Actual_Memory_Binding.

[0079] In another embodiment of the present application, the regional task sub-model obtained by constructing a model for the regional tasks included in each system partition of the system to be evaluated may include the following three sub-models: (1) Task model TM = {T}, where. Considering the accurate measurement of real-time performance, it is necessary to model the activities of processes in the embedded system. If the activities of processes cannot be modeled, the model cannot accurately simulate the real call state of threads. Since the overall structure of the behavior of the behavior annex is a non-deterministic hierarchical automaton, and its declared behavior structure includes states and transitions including conditions and operations, the behavior annex of AADL is used here to model the activities in the process. (2) Subroutine call model (SCM), that is, SCM = {S}, where S ∈ SubprogramSet, and (3) Internal behavior model (IBM), that is, IBM = {S, G, A, T, δ}, where: S represents a series of behavior states, G represents the set of monitoring state conditions, A represents a series of action sets, T is the set of transitions between behavior states, representing the mapping relationship between states, that is, δ: S×G → S×A.

[0080] Furthermore, to ensure that the initial performance parameters corresponding to at least two system tasks in the system to be evaluated can be predicted smoothly in the future, the regional task sub-model may further include the following three sub-models: task activity process model, task state model, and runtime task dynamic model.

[0081] Specifically, for the task activity process model, the activity process of the task describes the running process and basic functions of the embedded software system. The system running process and runtime state are indispensable parts for dynamic behavior analysis. In fact, when performing dynamic behavior analysis, it is mainly to analyze the behavior of the system during the running process. The activity process of the task is the most basic manifestation of the system behavior and the behavioral basis for dynamic analysis. In the task process model, the subroutines that make up the service process are abstract, and these abstract subroutines may be completed by specific individual tasks or task combinations to perform corresponding functions. Among them, when establishing the task activity process model, this article assumes that the embedded software system has a unique starting point and a unique ending point during the running process. Considering the situation that the embedded software system may have multiple starting points and multiple ending points, a virtual starting point and a virtual ending point can be constructed, such as the startup and termination of the embedded software system, to represent the start and end processes of the entire system. The purpose of constructing the starting point and the ending point is to ensure that the established service activity process has a main trunk and clarify the state of the system.

[0082] Among them, the task activity process model can be divided into an abstract task process and an actual task process. The abstract task process is an abstract description of the running process of the embedded software system, mainly depicting the abstract business process; for each abstract service in the abstract task process, a specific actual service process is established for description, and the actual task process clarifies the subroutines participating in the abstract task.

[0083] Specifically, for the task state model, the task state model is used to describe the current state of the system to be evaluated and the transitions between task states. Whether the task is in the called state is the main factor affecting the system real-time performance and resource utilization rate. The call of the task necessarily corresponds to the task response time and resource occupation. The task state model describes and models the task in the called state, suspended state, transitions between tasks, and redundant task set. The state transition of the task is described using two parameters: task execution time and CPU occupancy rate.

[0084] Specifically, for the runtime task dynamic model, the runtime task dynamic model is used to describe the interaction between tasks during task runtime. Based on the process of the software architecture, the runtime task dynamic model takes time into consideration to achieve the consideration of real-time performance. The main real-time parameters considered are task response time and task execution time. Among them, the task execution time reflects the working condition of the task itself, and the task response time reflects the current system condition.

[0085] Among them, as Figure 4 shown, this model contains three subroutine services: Service1, Servic2, and Service3. In the runtime task dynamic model, two parameters, task execution time and task response time, are extended. Among them, the task response time is the sum of the task execution time and the data transmission time.

[0086] S302. Transform the initial system model into the Petri net form to obtain the target system model in the Petri net form.

[0087] It should be noted that the initial system model is a model obtained by constructing the internal structure information and external environment information of the system to be evaluated according to the AADL language. Among them, AADL is a semi-formal modeling language with a defined semantic definition and a strict grammar language expression specification. Since AADL is only a modeling language that only describes the corresponding attributes of components, such as thread execution time description, component security level description, component error model appendix library, etc., if you want to analyze the corresponding non-functional attributes of the AADL system architecture model, you also need to formally describe the component attributes in the AADL system architecture model, and then borrow formal theory methods and related tools to verify whether the non-functional attributes of the system model meet the requirements.

[0088] Furthermore, when this application transforms the initial system model into the Petri net form (i.e., the Petri net form), it constructs the formal semantic grammar of the Petri net with time and resource dynamic simulation, and uses the Petri net to describe the dynamic characteristics such as task execution time and resource storage call, laying a foundation for the intelligent verification of real-time performance and resource utilization rate.

[0089] Specifically, the conversion of the initial system model into the form of Petri net can be achieved according to the following rules. Rule 1: Convert all tasks into places of GSPN. Rule 2: Convert the subroutine calls in the task model within the partition of AADL into transitions in GSPN, where the events that follow the Poisson distribution are converted into timed transitions, and the events that obey the fixed probability are converted into instantaneous transitions. Rule 3: Convert the transitions of the processes in the hierarchical model into arcs from transitions to places or arcs from places to transitions. Rule 4: Convert the tasks at the initial or power-on state into places containing a token. Rule 5: For the subroutine call switch that follows the Poisson distribution, convert the state and program call into places, add a timed transition that conforms to the Poisson distribution, add a prohibited arc from the error output place to the transition, and add the corresponding arcs. Rule 6: For the subroutine call switch that obeys the fixed probability, convert the state and program call into places, add a place representing the state of subroutine switch failure, add two transitions representing successful and failed subroutine calls respectively, and add the corresponding arcs. Rule 7: Concurrency can be described using two timed transitions. Both tasks can obtain tokens to execute. The Petri net structure of the converted parallel structure is shown in the following table. S2 and S3 represent two parallel tasks, with timed transitions T1 and T2 respectively. The parallel structure represents tasks that are parallel at the same moment. Rule 8: Interruption. In an embedded system, when some tasks with higher priorities preempt tasks with lower priorities, an interruption will be triggered, causing the currently executed task to be preempted by a task with a higher priority to obtain resources and run. The Petri net structure of the converted task interruption is shown in the following table. When task S1 runs and starts a subroutine controlled by timed transition T1, if a token reaches task S2 before the execution of timed transition T1, the task will be called. Since the instantaneous transition has a higher priority than the timed transition, timed transition T1 will not be executable, while instantaneous transition T2 will be executable, and the operation of task S2 interrupts the operation of task S1. Rule 9: Redundancy, that is, mutual fault tolerance, can be regarded as the parallel connection of two places. When one path fails, the other redundant structure will preempt the currently faulty task and re-execute it to complete the entire process.

[0090] The above system performance evaluation method realizes the conversion of the initial system model into the form of Petri net by constructing the initial system model corresponding to the system to be evaluated, obtaining the target system model in the form of Petri net, ensuring the subsequent smooth execution of the operation of predicting the initial performance parameters corresponding to at least two system tasks in the system to be evaluated, and ensuring that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, so as to accurately obtain the target performance parameters of the system to be evaluated.

[0091] In one embodiment, as Figure 5As shown in the figure, when it is necessary to evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task and obtain the target performance parameters of the system to be evaluated, the following specific contents may be included:

[0092] S501. Construct the state transition probability matrix corresponding to each system task according to the initial performance parameters corresponding to each system task.

[0093] Specifically, when constructing the state transition probability matrix corresponding to each system task, the isomorphism between Markov and the target system model can be utilized to construct the state transition probability matrix of the system to be evaluated, so as to evaluate the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters and obtain the target performance parameters of the system to be evaluated.

[0094] Among them, before constructing the state transition probability matrix corresponding to each system task, the following rules need to be preset: any failure of a component in the system to be evaluated is regarded as a system failure; the component failures in the system to be evaluated are independent of each other. This assumption has two implications: that is, the embedded system can be regarded as composed of a series of logically independent components, and these components can be implemented or tested independently. In addition, whether a component fails during system operation will not be affected by other components; the control transfer between components of the system to be evaluated follows a discrete-time Markov process. The transfer probability between components is determined by the state transition in the Petri net model. The component to be run at the next moment, that is, the control transfer between components, is only related to the state defined by the currently running component and has nothing to do with the history before this state.

[0095] In summary, when constructing the state transition probability matrix corresponding to each system task, the system to be evaluated is simulated. If the system has n task states, an n-dimensional vector Mi = (s1, s2,..., sn) can be defined to describe the system operation state. If the task is in the running state, then set si to 1, otherwise set it to 0.

[0096] Assume that the system consists of n different types of tasks. The execution of each independent task i (i = 1,..., n) is regarded as a state in the Markov process chain. pij represents the transfer probability from task i to task j, and Ri represents the real-time performance or resource call rate of object i. In this way, the probability that the system control flow transfers from object i to object j is Ripij, which represents the probability of transferring to object j after object i is successfully executed, so as to characterize information such as whether the system task execution time exceeds the limit and whether the resource call is successful, that is, the evaluation of the system real-time performance or resource call rate.

[0097] Suppose the system consists of n independent objects {C1, C2, …, Cn}, where C1 is the starting object and Cn is the terminating object, that is, after the system runs to object Cn, it will no longer transfer to the remaining objects. The state space of the Markov process chain is correspondingly {S1, S2, …, Sn}, then the initial transition probability matrix can be determined as follows:

[0098]

[0099] Expand the initial transition probability matrix by adding two absorbing states S and F, where S represents the state where the system correctly completes execution and ends, and S can only be transferred from the terminating object Cn, and F represents that a failure occurs during the system execution and can be transferred from any object. In this way, the state space of the Markov chain is expanded to {S, F, S1, S2, …, Sn}, then the state transition probability matrix can be determined as follows:

[0100]

[0101] S502, according to the state transition probability matrix and the initial performance parameters, evaluate the system performance of the system to be evaluated, and obtain the target performance parameters of the system to be evaluated.

[0102] It should be noted that the initial performance parameters include the initial aging parameter and the initial utilization parameter. Therefore, when it is necessary to evaluate the system performance of the system to be evaluated, the following contents can be included: according to the initial aging parameter and the state transition probability matrix, conduct an aging evaluation on the system to be evaluated to obtain the target aging parameter in the target performance parameters; according to the initial utilization parameter and the state transition probability matrix, conduct a utilization evaluation on the system to be evaluated to obtain the target utilization parameter in the target performance parameters.

[0103] In an embodiment of the present application, according to the state transition probability matrix, it can be known that the state transition probability matrix is obtained by adding the first two columns to the initial transition probability matrix, where the first column is the probability that the state of each object transfers to state S, and the second column is the transition probability that the state of each object transfers to state F. In the state transition probability matrix, the elements of the last row are 0, and in the matrix, the sum of the elements of the last row is still 0, which means that the terminating object Cn can only transfer to state S or F.

[0104] Use the following formula to calculate the target aging parameter (i.e., Rs in the formula) and the target utilization parameter (i.e., Qs in the formula) in the target performance parameters:

[0105]

[0106] Where I is the n-dimensional identity matrix, E is the matrix obtained by deleting the nth row and the first column from the I - M matrix, and Rn and Qn are the real-time performance and resource utilization rate of the nth reachable state.

[0107] The above system performance evaluation method constructs a state transition probability matrix corresponding to each system task, so as to evaluate the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters, and obtain the target performance parameters of the system to be evaluated, ensuring that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, and the target performance parameters of the system to be evaluated can be accurately obtained.

[0108] In an embodiment of the present application, as Figure 6 shown, when it is necessary to determine the target performance parameters of the system to be evaluated, the following contents may be included:

[0109] S601, construct a first internal model of the system to be evaluated according to the system interface information.

[0110] S602, construct a second internal model of the system to be evaluated according to the system hierarchy information.

[0111] S603, construct a first external model of the system to be evaluated according to the system scenario information.

[0112] S604, construct a second external model of the system to be evaluated according to the operating environment information.

[0113] S605, perform integration processing on the first internal model, the second internal model, the first external model and the second external model to obtain an initial system model corresponding to the system to be evaluated.

[0114] S606, perform Petri net form transformation on the initial system model to obtain a target system model in Petri net form.

[0115] S607, predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model.

[0116] S608, construct a state transition probability matrix corresponding to each system task according to the initial performance parameters corresponding to each system task.

[0117] S609, perform timeliness evaluation on the system to be evaluated according to the initial timeliness parameters and the state transition probability matrix, and obtain the target timeliness parameter in the target performance parameters.

[0118] S610, perform utilization evaluation on the system to be evaluated according to the initial utilization parameters and the state transition probability matrix, and obtain the target utilization parameter in the target performance parameters.

[0119] The above system performance evaluation method predicts the initial performance parameters corresponding to at least two system tasks in the system to be evaluated through the target system model corresponding to the system to be evaluated. Furthermore, the system performance of the system to be evaluated is evaluated according to the initial performance parameters corresponding to each system task, and the target performance parameters of the system to be evaluated are obtained. According to the above content, since the target system model corresponding to the system to be evaluated is constructed based on the internal structure information and external environment information of the system to be evaluated, the initial performance parameters corresponding to each system task predicted according to the target system model can accurately reflect the performance of each system task in the system to be evaluated. Furthermore, it is ensured that the target performance parameters of the system to be evaluated determined according to the initial performance parameters corresponding to each system task can also accurately reflect the actual situation of the system to be evaluated, improving the evaluation accuracy of evaluating the system performance of the system to be evaluated, and ensuring that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, and the target performance parameters of the system to be evaluated can be accurately obtained.

[0120] It should be understood that although the steps in the flowcharts involved in the above embodiments are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0121] Based on the same inventive concept, the embodiments of the present application also provide a system performance evaluation device for implementing the above-mentioned system performance evaluation method. The implementation solutions for solving problems provided by this device are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the system performance evaluation device provided below can refer to the limitations on the system performance evaluation method in the above text, and will not be repeated here.

[0122] In one embodiment, as Figure 7 shown, a system performance evaluation device is provided, including: a construction module 10, a prediction module 20, and an evaluation module 30, where:

[0123] The construction module 10 is configured to construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated.

[0124] A prediction module 20, configured to predict initial performance parameters corresponding to at least two system tasks in a system to be evaluated according to a target system model.

[0125] An evaluation module 30, configured to evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task, so as to obtain target performance parameters of the system to be evaluated.

[0126] In one embodiment, an initial system model corresponding to the system to be evaluated is constructed according to the internal structure information and external environment information of the system to be evaluated; the initial system model is transformed into a Petri net form to obtain a target system model in Petri net form.

[0127] In one embodiment, a first internal model of the system to be evaluated is constructed according to system interface information; a second internal model of the system to be evaluated is constructed according to system hierarchy information; a first external model of the system to be evaluated is constructed according to system scenario information; a second external model of the system to be evaluated is constructed according to operating environment information; the first internal model, the second internal model, the first external model, and the second external model are integrated to obtain an initial system model corresponding to the system to be evaluated.

[0128] In one embodiment, according to system hierarchy information, model construction is performed on system partitions included in the system to be evaluated to obtain system area sub-models; according to system hierarchy information, model construction is performed on area tasks included in each system partition of the system to be evaluated to obtain area task sub-models; the system area sub-models and the area task sub-models are integrated to obtain a second internal model of the system to be evaluated.

[0129] In one embodiment, a state transition probability matrix corresponding to each system task is constructed according to the initial performance parameters corresponding to each system task; according to the state transition probability matrix and the initial performance parameters, the system performance of the system to be evaluated is evaluated to obtain target performance parameters of the system to be evaluated.

[0130] In one embodiment, according to the initial aging parameter and the state transition probability matrix, the system to be evaluated is evaluated for aging to obtain a target aging parameter in the target performance parameters; according to the initial utilization parameter and the state transition probability matrix, the system to be evaluated is evaluated for utilization to obtain a target utilization parameter in the target performance parameters.

[0131] The above system performance evaluation device predicts the initial performance parameters corresponding to at least two system tasks in the system to be evaluated through the target system model corresponding to the system to be evaluated. Furthermore, the system performance of the system to be evaluated is evaluated according to the initial performance parameters corresponding to each system task, and the target performance parameters of the system to be evaluated are obtained. According to the above content, since the target system model corresponding to the system to be evaluated is constructed based on the internal structure information and external environment information of the system to be evaluated, the initial performance parameters corresponding to each system task predicted according to the target system model can accurately reflect the performance of each system task in the system to be evaluated. Furthermore, it is ensured that the target performance parameters of the system to be evaluated determined according to the initial performance parameters corresponding to each system task can also accurately reflect the actual situation of the system to be evaluated, improving the evaluation accuracy of the system performance evaluation of the system to be evaluated, and ensuring that when evaluating the performance of the system to be evaluated, the internal structure information and external environment information of the system to be evaluated can be fully referred to, so as to accurately obtain the target performance parameters of the system to be evaluated.

[0132] Each module in the above system performance evaluation device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0133] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 8As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a system performance evaluation method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0134] Those skilled in the art can understand that Figure 8 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0135] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0136] According to the internal structure information and external environment information of the system to be evaluated, construct a target system model corresponding to the system to be evaluated;

[0137] According to the target system model, predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated;

[0138] Evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task, and obtain the target performance parameters of the system to be evaluated.

[0139] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0140] Construct an initial system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0141] Perform a Petri net form transformation on the initial system model to obtain a target system model in Petri net form.

[0142] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0143] Construct a first internal model of the system to be evaluated according to the system interface information;

[0144] Construct a second internal model of the system to be evaluated according to the system hierarchy information;

[0145] Construct a first external model of the system to be evaluated according to the system scenario information;

[0146] Construct a second external model of the system to be evaluated according to the operating environment information;

[0147] Perform an integration process on the first internal model, the second internal model, the first external model, and the second external model to obtain an initial system model corresponding to the system to be evaluated.

[0148] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0149] Construct a system area sub-model by constructing a model for the system partitions included in the system to be evaluated according to the system hierarchy information;

[0150] Construct a regional task sub-model by constructing a model for the regional tasks included in each system partition of the system to be evaluated according to the system hierarchy information;

[0151] Perform an integration process on the system area sub-model and the regional task sub-model to obtain a second internal model of the system to be evaluated.

[0152] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0153] Construct a state transition probability matrix for each system task according to the initial performance parameters corresponding to each system task;

[0154] Evaluate the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters to obtain the target performance parameters of the system to be evaluated.

[0155] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0156] Perform a timeliness evaluation on the system to be evaluated according to the initial timeliness parameters and the state transition probability matrix to obtain the target timeliness parameter among the target performance parameters;

[0157] According to the initial utilization rate parameter and the state transition probability matrix, the utilization rate of the system to be evaluated is evaluated to obtain the target utilization rate parameter in the target performance parameters.

[0158] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0159] According to the internal structure information and external environment information of the system to be evaluated, a target system model corresponding to the system to be evaluated is constructed;

[0160] According to the target system model, the initial performance parameters corresponding to at least two system tasks in the system to be evaluated are predicted;

[0161] According to the initial performance parameters corresponding to each system task, the system performance of the system to be evaluated is evaluated to obtain the target performance parameters of the system to be evaluated.

[0162] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0163] According to the internal structure information and external environment information of the system to be evaluated, an initial system model corresponding to the system to be evaluated is constructed;

[0164] The initial system model is transformed into the form of a Petri net to obtain a target system model in the form of a Petri net.

[0165] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0166] According to the system interface information, a first internal model of the system to be evaluated is constructed;

[0167] According to the system hierarchy information, a second internal model of the system to be evaluated is constructed;

[0168] According to the system scenario information, a first external model of the system to be evaluated is constructed;

[0169] According to the operating environment information, a second external model of the system to be evaluated is constructed;

[0170] The first internal model, the second internal model, the first external model and the second external model are integrated to obtain an initial system model corresponding to the system to be evaluated.

[0171] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0172] According to the system hierarchy information, model construction is carried out on the system partitions included in the system to be evaluated to obtain a system area sub-model;

[0173] Construct a regional task sub - model for the regional tasks included in each system partition of the system to be evaluated according to the system - level information;

[0174] Integrate the system regional sub - model and the regional task sub - model to obtain the second internal model of the system to be evaluated.

[0175] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0176] Construct a state - transition probability matrix for each system task according to the initial performance parameters corresponding to each system task;

[0177] Evaluate the system performance of the system to be evaluated according to the state - transition probability matrix and the initial performance parameters to obtain the target performance parameters of the system to be evaluated.

[0178] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0179] Evaluate the timeliness of the system to be evaluated according to the initial timeliness parameters and the state - transition probability matrix to obtain the target timeliness parameter in the target performance parameters;

[0180] Evaluate the utilization rate of the system to be evaluated according to the initial utilization rate parameters and the state - transition probability matrix to obtain the target utilization rate parameter in the target performance parameters.

[0181] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0182] Construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0183] Predict the initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model;

[0184] Evaluate the system performance of the system to be evaluated according to the initial performance parameters corresponding to each system task to obtain the target performance parameters of the system to be evaluated.

[0185] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0186] Construct an initial system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated;

[0187] Convert the initial system model into the form of a Petri net to obtain the target system model in the form of a Petri net.

[0188] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0189] Construct a first internal model of the system to be evaluated according to the system interface information;

[0190] Construct a second internal model of the system to be evaluated according to the system hierarchy information;

[0191] Construct a first external model of the system to be evaluated according to the system scenario information;

[0192] Construct a second external model of the system to be evaluated according to the operating environment information;

[0193] Integrate the first internal model, the second internal model, the first external model, and the second external model to obtain an initial system model corresponding to the system to be evaluated.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0195] Construct a system area sub-model by constructing a model for the system partitions included in the system to be evaluated according to the system hierarchy information;

[0196] Construct a regional task sub-model by constructing a model for the regional tasks included in each system partition of the system to be evaluated according to the system hierarchy information;

[0197] Integrate the system area sub-model and the regional task sub-model to obtain a second internal model of the system to be evaluated.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0199] Construct a state transition probability matrix for each system task according to the initial performance parameters corresponding to each system task;

[0200] Evaluate the system performance of the system to be evaluated according to the state transition probability matrix and the initial performance parameters to obtain the target performance parameters of the system to be evaluated.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0202] Evaluate the timeliness of the system to be evaluated according to the initial timeliness parameters and the state transition probability matrix to obtain the target timeliness parameter in the target performance parameters;

[0203] Evaluate the utilization rate of the system to be evaluated according to the initial utilization rate parameters and the state transition probability matrix to obtain the target utilization rate parameter in the target performance parameters.

[0204] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0205] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0206] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0207] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A system performance evaluation method, characterized in that: The method comprises: Constructing a target system model corresponding to the system to be evaluated based on the internal structure information and external environment information of the system to be evaluated; Predicting initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model; the initial performance parameters include initial timeliness parameters and initial utilization rate parameters; Constructing a state transition probability matrix corresponding to each of the system tasks according to the initial performance parameters corresponding to each of the system tasks; According to the initial timeliness parameter and the state transition probability matrix, the timeliness evaluation is performed on the system to be evaluated to obtain the target timeliness parameter in the target performance parameter corresponding to the system to be evaluated; Performing utilization evaluation on the system to be evaluated according to the initial utilization parameter and the state transition probability matrix to obtain a target utilization parameter in the target performance parameter corresponding to the system to be evaluated; Among them, the target time parameter R S and the target utilization parameter Q S Determined using the following formula: Where I is the n-dimensional identity matrix; E is the matrix after deleting the nth row and the first column from the IM matrix; M is the initial transition probability matrix; R n is the initial aging parameter; Q n is the initial utilization parameter; Among them, the state transition probability matrix is ​​obtained by adding the first two columns to the initial transition probability matrix; the first column is the transition probability of each object state transferring to the first state S, and the second column is the transition probability of each object state transferring to the second state F; the first state S represents the state in which the execution is completed correctly and ended; the second state F represents the state in which failure occurs during the execution process.

2. The method according to claim 1, characterized in that The step of constructing a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated comprises: Constructing an initial system model corresponding to the system to be evaluated based on the internal structure information and external environment information of the system to be evaluated; The initial system model is transformed into a Petri net form to obtain a target system model in a Petri net form.

3. The method according to claim 2, characterized in that The internal structure information includes system interface information and system hierarchy information; the external environment information includes system scenario information and operating environment information; the initial system model corresponding to the system to be evaluated is constructed according to the internal structure information and external environment information of the system to be evaluated, including: Constructing a first internal model of the system to be evaluated according to the system interface information; Constructing a second internal model of the system to be evaluated according to the system hierarchy information; Constructing a first external model of the system to be evaluated according to the system scenario information; Constructing a second external model of the system to be evaluated according to the operating environment information; The first internal model, the second internal model, the first external model and the second external model are integrated to obtain an initial system model corresponding to the system to be evaluated.

4. The method according to claim 3, characterized in that The step of constructing a second internal model of the system to be evaluated according to the system hierarchy information includes: According to the system hierarchy information, a model is constructed for the system partitions included in the system to be evaluated to obtain a system area sub-model; According to the system hierarchy information, a model is constructed for the regional tasks contained in each system partition of the system to be evaluated to obtain a regional task sub-model; The system area sub-model and the area task sub-model are integrated to obtain a second internal model of the system to be evaluated.

5. The method according to claim 4, characterized in that The system area sub-model includes a virtual processor component, a process component and a memory component.

6. The method according to claim 4, characterized in that The regional task sub-model includes: a task model, a subroutine calling model and an internal behavior model.

7. A system performance evaluation device, characterized in that: The device comprises: A construction module, used to construct a target system model corresponding to the system to be evaluated according to the internal structure information and external environment information of the system to be evaluated; A prediction module, used to predict initial performance parameters corresponding to at least two system tasks in the system to be evaluated according to the target system model; the initial performance parameters include initial timeliness parameters and initial utilization rate parameters; An evaluation module is used to construct a state transition probability matrix corresponding to each of the system tasks according to the initial performance parameters corresponding to each of the system tasks; perform a timeliness evaluation on the system to be evaluated according to the initial timeliness parameters and the state transition probability matrix to obtain a target timeliness parameter in the target performance parameters corresponding to the system to be evaluated; perform a utilization evaluation on the system to be evaluated according to the initial utilization parameters and the state transition probability matrix to obtain a target utilization parameter in the target performance parameters corresponding to the system to be evaluated; Among them, the target time parameter R S and the target utilization parameter Q S Determined using the following formula: Where I is the n-dimensional identity matrix; E is the matrix after deleting the nth row and the first column from the IM matrix; M is the initial transition probability matrix; R n is the initial aging parameter; Q n is the initial utilization parameter; Among them, the state transition probability matrix is ​​obtained by adding the first two columns to the initial transition probability matrix; the first column is the transition probability of each object state transferring to the first state S, and the second column is the transition probability of each object state transferring to the second state F; the first state S represents the state in which the execution is completed correctly and ended; the second state F represents the state in which failure occurs during the execution process.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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