System reliability analysis method and device, computer device and storage medium

Abstract

The application relates to a system reliability analysis method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: constructing an operator operation process model according to basic elements of a system operation process, the basic elements comprising operation resources, the operation resources comprising personnel resources and equipment resources, and the basic elements being obtained by defining the system operation process based on a Petri net model; determining a reliability quantization result of the personnel resources based on the operator operation process model; determining a failure probability of the equipment resources according to the failure rate and working time length of each equipment in the system; and determining the success probability of an operation task of the system according to the reliability quantization result and the failure probability. According to the scheme, the personnel resources and the equipment resources involved in the operation process of the system for completing the operation task are quantized into a reliability analysis model, and then the reliability of the system is analyzed, so that the influence of elements related to personnel participation on system reliability analysis is solved.

Description

Technical Field

[0001] This application relates to the field of system engineering reliability analysis technology, and in particular to a system reliability analysis method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the advancement of science and technology, especially the rapid development of high-tech such as computer and electronic technologies, the degree of automation in various systems is increasing. Due to this increasing automation, in complex systems involving human intervention, the role of personnel is gradually shifting towards decision-making, primarily monitoring. Simultaneously, with the continuous improvement of the reliability of internal hardware and software, the role of personnel in ensuring system reliability is becoming increasingly prominent. Therefore, the stable and efficient operation of a system is inseparable from human monitoring and operation. As a crucial component of the system besides hardware and software, personnel have an increasingly close relationship with internal equipment and play an increasingly important role in system design, operation, and management. The rational interaction between people and equipment is the core of ensuring the normal and stable operation of the system.

[0003] Common system reliability modeling methods mainly include fault trees, event trees, and Markov models. However, due to the complex human-machine dynamic interaction behavior in complex dynamic systems involving human participation, as well as the characteristics of multi-stage task processes, component polymorphism, and event correlation, the above-mentioned common system reliability modeling methods have many limitations in handling these characteristics. For example, the static combination model of fault trees or event trees is cumbersome in describing fault characteristics such as polymorphism, failure correlation, and nondeterministic logic; dynamic models based on dynamic fault trees and Markov models usually face the problem of system state space explosion when defining system states and transition matrices. Summary of the Invention

[0004] Therefore, it is necessary to provide a more effective system reliability analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product for systems involving human participation, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a system reliability analysis method. The method includes:

[0006] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0007] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0008] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0009] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0010] In one embodiment, the Petri net model includes four components: places, transitions, directed arcs, and tokens. The basic elements also include the logical relationships of operation steps and operation states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operation states are obtained by defining multiple operation state databases, and the logical relationships of operation steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Accordingly, based on the basic elements of the system operation process, an operator operation process model is constructed, including:

[0011] Multiple personnel resource warehouses, multiple equipment resource warehouses, and multiple operational status warehouses are treated as a warehouse set. The warehouse identifier and capacity function corresponding to each warehouse in the warehouse set are determined. The warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate.

[0012] Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results.

[0013] Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, an operation process model for operators is constructed.

[0014] In one embodiment, the reliability quantification result of personnel resources is determined based on the operator's operation process model, including:

[0015] By modeling the operator's operation process, the preset operation time for each step of the operation task is obtained; each step of the operation task is simulated multiple times to obtain the simulated operation time for each step in each simulation; based on the preset operation time for each step and the simulated operation time for each step in each simulation, the probability of operator response failure is calculated.

[0016] Based on the influence weight of the probability of personnel function failure in the system implementation environment, the probability of personnel function failure for each operation step is determined. The influence weight of the probability of personnel function failure is determined by the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the quantification results of the reliability of personnel resources.

[0017] In one embodiment, the duration of all simulated operations for each operation step during multiple simulations follows a normal distribution; based on the preset operation duration of each operation step and the simulated operation duration of each operation step in each simulation, the probability of operator response failure is calculated, including:

[0018] Based on the total simulation operation duration of each operation step during multiple simulations, construct the operation duration distribution function corresponding to each operation step.

[0019] Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining the random operation duration values ​​corresponding to each operation step based on the operation duration distribution function corresponding to each operation step.

[0020] Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success.

[0021] Count the number of times the operation response result is successful in multiple operation duration value sets, and calculate the probability of operation response failure based on the number of times.

[0022] In one embodiment, the operation task is composed of multiple sub-operation tasks, and all operation steps of the operation task are configured in the multiple sub-operation tasks respectively; the success probability of the system's operation task is determined based on the reliability quantification result and the failure probability, including:

[0023] Based on the logical relationship of the operation steps, the probability of personnel operation failure for each sub-operation task is calculated according to the probability of personnel function failure.

[0024] The success probability of an operation task is calculated based on the probability of personnel operation response failure, the probability of equipment resource failure, and the probability of personnel operation failure for each sub-operation task.

[0025] In one embodiment, based on the logical relationship of the operation steps and according to the probability of personnel functional failure, the probability of personnel operation failure for each sub-operation task is calculated, including:

[0026] If the logical relationship of the operation steps is serial, then the probability of personnel function failure for each operation step in each sub-operation task is multiplied together, and the result of the multiplication is taken as the probability of personnel operation failure for each sub-operation task.

[0027] If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task.

[0028] Secondly, this application also provides a system reliability analysis apparatus. The apparatus includes:

[0029] The building module is used to construct an operator operation process model based on the basic elements of the system operation process. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0030] The first determination module is used to determine the reliability quantification result of personnel resources based on the operator's operation process model;

[0031] The second determination module is used to determine the failure probability of equipment resources based on the failure rate and working time of each device in the system.

[0032] The third determination module is used to determine the success probability of the system's operational tasks based on the reliability quantification results and failure probability.

[0033] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0034] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0035] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0036] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0037] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0038] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0039] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0040] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0041] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0042] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0043] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0044] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0045] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0046] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0047] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0048] The aforementioned system reliability analysis methods, devices, computer equipment, storage media, and computer program products construct an operator operation process model based on the basic elements of the system operation process. These basic elements include operational resources, specifically personnel and equipment resources, defined using a Petri net model. Based on the operator operation process model, the reliability quantification results of personnel resources are determined. The failure probability of equipment resources is determined based on the failure rate and operating time of each device in the system. Finally, the success probability of the system's operational tasks is determined based on the reliability quantification results and failure probabilities. This solution quantifies both personnel and equipment resources involved in the system's operational tasks into the reliability analysis model, and uses these quantified resources to analyze system reliability, thus addressing the impact of personnel-related elements on system reliability analysis. Attached Figure Description

[0049] Figure 1 This is a diagram illustrating the application environment of a system reliability analysis method in one embodiment;

[0050] Figure 2 This is a flowchart illustrating a system reliability analysis method in one embodiment;

[0051] Figure 3 A schematic diagram of a Petri net model of device failure for a sub-operation task in one embodiment;

[0052] Figure 4 This is a flowchart illustrating the system reliability analysis method in another embodiment;

[0053] Figure 5 This is a schematic diagram of the Petri net model in one embodiment;

[0054] Figure 6 This is a flowchart illustrating the system reliability analysis method in yet another embodiment;

[0055] Figure 7 This is a schematic diagram illustrating the calculation process for the success probability of an operation task in one embodiment;

[0056] Figure 8 This is a structural block diagram of a system reliability analysis device in one embodiment;

[0057] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] First, the system mentioned in this application refers to a complex dynamic system involving human participation, primarily referring to systems that require human intervention to complete relevant operational tasks and whose behavior can be monitored, such as maintenance systems and driving systems. Second, system reliability analysis refers to estimating the probability of a system successfully performing its intended function during a specific operational task using a system reliability model.

[0060] The system reliability analysis method provided in this application can be applied to real-time system reliability analysis, or it can be used to analyze the reliability of a system after it has been established but before the actual system completes a certain operation task. This application does not specifically limit the application in this way. For ease of understanding, this application takes the example of analyzing the reliability of a system after it has been established but before the actual system completes a certain operation task. The method provided in this application can be used to perform reliability analysis on systems with human involvement. The method provided in this application can be applied to systems such as… Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0061] Terminal 102 is a device capable of acquiring all elements involved in the system operation process, and may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. Portable wearable devices may include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0062] In one embodiment, such as Figure 2 As shown, a system reliability analysis method is provided, which can be applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0063] Step 202: Based on the basic elements of the system operation process, construct an operator operation process model. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0064] The system in this embodiment is a complex dynamic system, meaning it involves human intervention during the completion of a specific operational task. This system is a dynamic process of executing operational tasks, such as a courier sorting system. The operational task is to pack each courier and accurately deliver it to the warehouse at the delivery location. The system is operational throughout the entire process, hence dynamic. However, in some operational steps, such as the courier packing step, human intervention is required. Based on the above description, it can be understood that the basic elements of the system operation process are the essential components for the system to complete its operational tasks, including human operation, equipment support, and other factors. Considering that the composition of the system in actual application scenarios will be more complex, in this embodiment, the operational resources among the basic elements only consider personnel and equipment.

[0065] In real-world applications, systems are complex and large-scale, existing as physical entities. Analyzing system reliability requires first transforming the entire system structure into a mathematical model. This model uses mathematical methods to describe the functional relationships between system units, enabling the calculation of the probability of success when the system completes a specific task. Petri nets are a modeling tool suitable for describing asynchronous, concurrent computer system models. They can be used to construct operator operation models of the system.

[0066] Specifically, the basic elements of the system's operation process are first defined and divided, and then the basic elements of the operation process are represented using Petri nets. During the operator's task execution, the elements involved include: operation status, operation resources, and the logical relationships between operation steps. Each component element is defined, and the basic elements of the operation process are described using Petri net elements. Then, based on the Petri net element representation method of the basic elements of the operation process, a CPN-based operation process model is constructed. CPN is an optimized Petri net modeling tool that can distinguish resources in the system by color.

[0067] Step 204: Based on the operator's operation process model, determine the reliability quantification results of personnel resources;

[0068] It should be noted that personnel resources refer to the cognitive and conscious resources of operators, which are divided into perceptual resources, cognitive resources, and executive resources. These elements are represented in the constructed operator operation process model. Therefore, based on the established operator operation process model, the reliability of personnel resources during operation is quantified. Specifically, the reliability of personnel in the task scenario is calculated in two parts. The first part analyzes the operational response time when a person performs operational steps; the second part calculates the probability of personnel functional failure under the coupled system implementation environment of personnel, equipment, and environment based on cognitive reliability and error analysis (CREAM). The quantification results of these two parts are used as input for the subsequent step 208.

[0069] For a given operation, each task consists of multiple steps. The probability of human error failure reflects the likelihood of a human error pattern occurring when a person performs a particular step. There are four types of human error patterns when an operator performs each step: skill-based perceptual error, skill-based execution error, knowledge-based cognitive error, and rule-based cognitive error.

[0070] It should be noted that in the skill-based behavior mode, operators can respond with almost no thought, similar to human instinct. Therefore, errors made by operators in the perception and execution phases are mainly skill-based errors. In the rule-based behavior mode, operators need to select certain rules and perform tasks according to the requirements of those rules. Therefore, errors made by operators in the identification and rule selection process during the cognitive phase are rule-based errors. In the knowledge-based behavior mode, operators need to rely on their own knowledge and experience to analyze, make decisions, and execute. Therefore, errors made by operators in the analysis, diagnosis, and decision-making process during the cognitive phase are knowledge-based errors.

[0071] Step 206: Determine the failure probability of equipment resources based on the failure rate and working time of each device in the system;

[0072] It's important to note that a complete system consists of multiple devices arranged in an "assembly line" configuration. Each device can be considered to complete a sub-task, and multiple sub-tasks (devices) constitute an overall task (system). Equipment failure refers to the temporary loss of a device's intended function during its lifespan due to wear, tear, or operational issues. The equipment failure rate is the probability of failure per unit of operating time. Because different devices have different usage times, structures, and requirements, they have different failure rates and operating times, and therefore, different failure probabilities.

[0073] Specifically, by transforming the functional units and operating states of each device in the system into a functional failure Petri net model, and based on this functional failure Petri net model, the failure probability of device resources can be calculated under a given simulation time, providing input for calculating the success probability of subsequent system operation tasks. It should be noted that the functional failure Petri net model and the operator operation process model mentioned in step 202 are two different models; therefore, this embodiment does not describe the construction process of the functional failure Petri net model.

[0074] Furthermore, functional failure models constructed based on different functional units of different devices can be divided into warm backup dual redundancy structures and hot backup dual redundancy structures. Taking a certain sub-operation task as an example, the device needs three functional units A, B, and C to execute the task. A, B, and C are connected in series; that is, if any one of A, B, or C fails, the task cannot be completed. Device A has a dual redundancy cold backup structure, device B has a dual redundancy warm backup structure, and device C has a dual redundancy hot backup structure. At the start of the task, none of the basic units in functional units A, B, and C have failed. The Petri net model for the device's failure is established as follows: Figure 3 As shown, by setting the equipment failure rate and simulation time, the failure probability of the equipment resources can be obtained. This failure probability is used as input for calculating the success probability of subsequent operation tasks, thus obtaining the final reliability analysis result of the operation task.

[0075] Step 208: Determine the success probability of the system's operational tasks based on the reliability quantification results and failure probability;

[0076] This step integrates the reliability quantification results of personnel resources calculated in steps 204 and 206 with the failure probability results of equipment resources. Based on the task success probability calculation model, the probability of successful operation is finally obtained as the system reliability quantification result.

[0077] In the above embodiment, an operator operation process model is constructed based on the basic elements of the system operation process. These basic elements include operational resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model. Based on the operator operation process model, the reliability quantification result of personnel resources is determined. The failure probability of equipment resources is determined based on the failure rate and working time of each device in the system. Finally, the success probability of the system's operation task is determined based on the reliability quantification result and the failure probability. This solution quantifies both personnel and equipment resources involved in the system's operation process into the reliability analysis model, and uses the quantified personnel and equipment resources to analyze system reliability, thus addressing the impact of personnel-related elements on system reliability analysis.

[0078] Based on the above embodiments, the Petri net model includes four components: places, transitions, directed arcs, and tokens. The basic elements also include the logical relationships of operation steps and operation states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operation states are obtained by defining multiple operation state databases, and the logical relationships of operation steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Accordingly, see [link to relevant documentation]. Figure 4 Based on the basic elements of the system operation process, an operator's operation process model is constructed, including:

[0079] Step 402: Collect multiple personnel resource warehouses, multiple equipment resource warehouses, and multiple operation status warehouses as a warehouse set, and determine the warehouse identifier and capacity function corresponding to each warehouse in the warehouse set; the warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate.

[0080] Step 404: Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results.

[0081] Step 406: Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, construct the operation process model of the operator.

[0082] First, the Petri net model comprises four components: places, transitions, directed arcs, and tokens. Structurally, it is a directed bipartite graph containing two types of nodes: place nodes and transition nodes. See also Figure 5In graphical representation, place nodes are represented by small circles, while transition nodes are represented by small rectangles or short horizontal lines. The relationship between place nodes and transition nodes is represented by directed edges. To describe their dynamic behavior, small black dots called tokens are introduced into the network, which exist in some place nodes. The initial distribution of tokens in the set of places is called the initial identifier. Tokens can flow in the network according to the following rules, thus representing the dynamic information flow characteristics of the system: a transition in the network is only triggered if all its input places (there are directed edges between place nodes and transition nodes) contain enough tokens; once the transition is triggered, tokens flow out of each of its input places, and tokens flow into each of the transition's output places. Figure 5 The left side shows a Petri net with initial labels, where transitions are triggerable. The Petri net after a transition is triggered and its labels are as follows: Figure 5 As shown on the right.

[0083] In conjunction with the application in this application, the process of representing the basic elements of the operation process and constructing the operator's operation process model using Petri nets is as follows:

[0084] (1) Definition of operation state and representation of its Petri net elements:

[0085] Operation status represents the completion status of each operation step, and is represented by "places" in the Petri net model.

[0086] make Let n be the number of operation state libraries, and let n be the number of operation state libraries. The identification and capacity function expression of the operation state library are as follows:

[0087]

[0088] In the formula, The color of the operation status library. The number of operations entrusted to the state library.

[0089] (2) Definition of operational resources and representation of Petri net elements

[0090] make Let the set of cognitive resource repositories of operators be represented. Then the repository identifier and capacity function expression are:

[0091]

[0092] In the formula, For the i-th type of cognitive resource base The color of the Middle Token; For a certain state of the warehouse The number of cognitive resources available in the system; Let be the maximum value of the i-th type of cognitive resource in the repository. Similarly, the sets of repositories for perceptual resources and executive resources can be obtained. The set of repositories for perceptual resources, cognitive resources, and executive resources constitutes the set of repositories for personnel resources. .

[0093] make Let the set of equipment resources be represented. Then the identification and capacity function expression of the equipment resource library are:

[0094]

[0095] In the formula, For the j-th type of equipment resource library The color of the Middle Token; For a certain state of the warehouse The number of available device resources in the system; It represents the maximum value of the i-th type of equipment resource in the warehouse.

[0096] (3) Representation of the logical relationship of operation steps

[0097] The logical relationships between operation steps mainly include serial relationships, parallel relationships, and selection relationships. It means that among them , , for and The logical relationship, in which, A set representing change.

[0098] (4) Constructing an operator's operation process model

[0099] Based on the Petri net element representation method of the components of the operation process, an operator's operation process model is constructed. The specific implementation process is as follows:

[0100] Define 8-element combinations A model of an operator's operational process is provided if and only if:

[0101] ① For CPN;

[0102] ② , Indicates the collection of operational status databases, Indicates the collection of personnel resources, This represents the collection of equipment resources.

[0103] ③ A set representing change;

[0104] ④ It is the set of directed arcs between resource repositories and changes;

[0105] ⑤ For the color set of the operation model, These represent the color identifier sets in the operation status database, the personnel resource color identifier set, and the equipment resource database, respectively.

[0106] ⑥ , for and The logical relationship;

[0107] ⑦ Identification The capacity function corresponds to each set of collections.

[0108] ⑧ W is the weight function, and the directed arc of the operation state library represents the logical relationship between the operation state and the operation step.

[0109] The weight function expressions for personnel resources and equipment resources are as follows:

[0110]

[0111]

[0112] In the above expression, , It is a non-negative integer, and not all of them are 0. This represents the weight of the i-th type of personnel resource. This represents the weight of the i-th type of equipment resource.

[0113] In the above embodiment, multiple personnel resource locations, multiple equipment resource locations, and multiple operational status locations are considered as a location set. A location identifier and capacity function are determined for each location in the location set. The location identifier is used to uniquely identify the location, and the capacity function is used to calculate the number of tokens that can be accommodated in the location. The transition data set of the adjacent directed arcs for each location is determined, and the weight function of the adjacent directed arcs for each location is determined based on the location classification results. Based on the location set, transition data set, directed arc set, location classification results, location identifier, capacity function, weight function, and the logical relationship of the operational steps, an operator's operational process model is constructed. This solution quantifies both personnel and equipment resources involved in the system's operation process into the reliability analysis model, providing a model foundation for subsequent reliability analysis using the quantified personnel and equipment resources.

[0114] Referring to the content of the above embodiments, see Figure 6 Based on the operator's operational process model, the reliability quantification results of personnel resources are determined, including:

[0115] Step 602: Obtain the preset operation time of each operation step of the operation task through the operator operation process model; simulate the execution of each operation step of the operation task multiple times to obtain the simulated operation time of each operation step in each simulation execution; and calculate the probability of failure of the operator operation response based on the preset operation time of each operation step and the simulated operation time of each operation step in each simulation execution.

[0116] Step 604: Determine the probability of personnel function failure for each operation step based on the influence weight of the probability of personnel function failure in the system implementation environment. The influence weight of the probability of personnel function failure is determined by the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the quantification results of the reliability of personnel resources.

[0117] In this system, because operators' skill levels vary, and the distance between the operator and the equipment within the system is also relevant, multiple simulations of the operational parameters are necessary to obtain a more accurate probability of operator failure. For each operational step, if the simulated operation time for each step exceeds the preset time, the operation is considered a failure. If the simulated operation time for each step is no greater than the preset time, the operator is considered to have completed the operation within the allowed time.

[0118] Specifically, in one embodiment, a Monte Carlo-based simulation model of human operation response time is first established. This model simulates the operation time for each step of a human's task, calculated using the following formula:

[0119]

[0120] In the formula, T represents the human operation response time; T1 represents the information perception time, which is generally taken as a fixed value of 85ms; and T2 represents the cognitive processing time. c=0.01, hj is the frequency of target j, dj is the distance between target j and the operator's fovea, expressed as a visual angle; T3 is the action execution time, its value is obtained through the model method, and the expression is: , K is the model value, K is the number of models, and R is the allowance rate. The allowance rate takes into account the impact of factors such as fatigue on the operation response time, and is generally taken as 22%.

[0121] In the method provided in the above embodiments, the preset operation time of each operation step of the operation task is obtained through an operator operation process model; each operation step of the operation task is simulated multiple times to obtain the simulated operation time of each operation step in each simulation execution; based on the preset operation time of each operation step and the simulated operation time of each operation step in each simulation execution, the probability of personnel operation response failure is calculated; based on the influence weight of the personnel function failure probability in the system implementation environment, the probability of personnel function failure probability is determined, and the influence weight of the personnel function failure probability is determined through the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the reliability quantification results of personnel resources. By quantifying the time for personnel to execute operation steps and the cognitive reliability of personnel, input is provided for subsequent system reliability calculations involving personnel participation.

[0122] In conjunction with the above embodiments, in one embodiment, the duration of all simulated operations for each operation step during multiple simulations follows a normal distribution; based on the preset operation duration of each operation step and the simulated operation duration of each operation step in each simulation, the probability of personnel operation response failure is calculated, including:

[0123] Based on the total simulation operation duration of each operation step during multiple simulations, construct the operation duration distribution function corresponding to each operation step.

[0124] Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining the random operation duration values ​​corresponding to each operation step based on the operation duration distribution function corresponding to each operation step.

[0125] Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success.

[0126] Count the number of times the operation response result is successful in multiple operation duration value sets, and calculate the probability of operation response failure based on the number of times.

[0127] In this scenario, when an operator performs a task, the initial available operation time is T, where T is the sum of the preset durations of all operation steps. The task includes m operation steps, and the operation time of the i-th operation step is ti. When the operation steps are sequential, meaning the next operation step can only be executed after the previous one is completed, the task's operation response time is... If it's a parallel relationship, meaning several operation steps can be performed simultaneously, then... If it's a selection relationship, then the operation time of the selected path is the operation response time of the task. It should be noted that t and T here only represent duration.

[0128] Specifically, in one embodiment, taking a simulation to represent a set of operation duration values ​​as an example, a successful simulation means that each operation step is successful. Let the sampling time of the operation response for the i-th operation step be denoted as... After successfully responding to the first i operation steps, the available operation time is Assuming the task starts at time t=0, the sampled operation response time is... First operation response time sample value ,if Record a simulation failure and restart the simulation; conversely, record the sampled value of the operation response time. ,if If the response fails, record the failure again and restart the simulation. Similarly, execute the simulation a specified number of times until the condition is met. At that time, a successful event simulation is obtained.

[0129] If the number of simulations is M and the number of failures is N, then the probability of the operation response failing is:

[0130]

[0131] The probability of a successful operation response is:

[0132]

[0133] In the method provided in the above embodiments, an operation duration distribution function corresponding to each operation step is constructed based on all simulated operation durations during multiple simulated executions of each operation step; multiple sets of operation duration values ​​are constructed; each set of operation duration values ​​is obtained by combining a random operation duration value corresponding to each operation step based on the operation duration distribution function corresponding to each operation step; the operation response result corresponding to each set of operation duration values ​​is determined based on the preset operation duration of each operation step; the operation response result is either failure or success; the number of successful operation responses in the multiple sets of operation duration values ​​is counted, and the probability of personnel operation response failure is calculated based on the number of times. This quantifies the impact of personnel operation time on system reliability, providing input for subsequent reliability calculations of systems involving personnel.

[0134] Referring to the content of the above embodiments, see Figure 7In one embodiment, the operation task is composed of multiple sub-operation tasks, and all operation steps of the operation task are configured in the multiple sub-operation tasks respectively; the success probability of the system's operation task is determined based on the reliability quantification result and the failure probability, including:

[0135] Step 702: Based on the logical relationship of the operation steps, calculate the probability of personnel operation failure for each sub-operation task according to the probability of personnel function failure.

[0136] Step 704: Calculate the success probability of the operation task based on the failure probability of personnel operation response, the failure probability of equipment resources, and the failure probability of personnel operation for each sub-operation task.

[0137] It should be noted that for a given operation task, each operation task consists of n sub-operation tasks. Therefore, the logical relationship between the operation tasks and the operation steps must first be analyzed. Only based on the results of this logical relationship analysis can the probability of human operation failure for the operation task be quantitatively calculated. Taking an operation sub-task with n serial processes as an example, human operation failure, untimely operation, and equipment resource failure in the sub-operation task will all lead to task failure. The formula for calculating the success probability of the operation task is as follows:

[0138]

[0139] in, Let i be the probability of human function failure when performing the i-th operation task. Let n be the probability of personnel failing to complete n sub-tasks. Available operation time The probability of personnel operation response failure within the system. The probability of equipment resource failure within a specified task time T, where the specified task time T is the time from when the equipment is put into operation to when the task ends.

[0140] In the method provided in the above embodiments, based on the logical relationship of the operation steps, the probability of personnel operation failure for each sub-operation task is calculated according to the probability of personnel function failure; the success probability of the operation task is calculated based on the probability of personnel operation response failure, the probability of equipment resource failure, and the probability of personnel operation failure for each sub-operation task. By quantifying both personnel and equipment resources involved in the system's operation process into the reliability analysis model, and using the quantified personnel and equipment resources to analyze system reliability, the impact of elements related to personnel participation on system reliability analysis is resolved.

[0141] In conjunction with the above embodiments, in one embodiment, based on the logical relationship of the operation steps and according to the probability of personnel function failure, the probability of personnel operation failure for each sub-operation task is calculated, including:

[0142] If the logical relationship of the operation steps is serial, then the probability of personnel function failure for each operation step in each sub-operation task is multiplied together, and the result of the multiplication is taken as the probability of personnel operation failure for each sub-operation task.

[0143] If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task.

[0144] Specifically, in conjunction with the explanation in step 204, when calculating the probability of personnel functional failure, the error mode is divided into three forms: perception, cognition (including knowledge-based cognitive errors and rule-based cognitive errors), and execution. The corresponding weighting factors affecting the probability of personnel functional failure are obtained, and then the probability of personnel functional failure is calculated. Let these represent human perception, cognition, and executive functions, respectively. Then the calculation formula is:

[0145]

[0146] in, Let be the failure probability of the i-th type of function for the operator. The influence weight of the probability of functional failure of human beings in the system implementation environment is obtained by the CREAM method.

[0147] In practical applications, the logical relationships between each operation step in each sub-task may be multiple or singular. However, the principle remains the same when calculating the probability of human operation failure. Taking the simplest case as an example, if sub-task Ti consists of two operation steps t1 and t2, and the operation relationship is sequential, then the probability of human operation failure in sub-task Ti is... for:

[0148]

[0149] In the formula, and The probability of personnel failure in operation steps t1 and t2.

[0150] If a sub-task Ti consists of two operation steps t1 and t2, and they are parallel operations, then the probability of human operation failure for sub-task Ti is... for:

[0151]

[0152] In the formula, and The probability of personnel failure in operation steps t1 and t2.

[0153] In the method provided in the above embodiments, if the logical relationship of the operation steps is serial, the personnel function failure probability of each operation step in each sub-operation task is multiplied, and the result of the multiplication is taken as the personnel operation failure probability of each sub-operation task; if the logical relationship of the operation steps is parallel, the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task. By combining the actual relationship between each operation step of the system, the errors made by personnel during the identification operation process are quantified, providing input for subsequent reliability calculations of systems involving personnel.

[0154] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0155] Based on the same inventive concept, this application also provides a system reliability analysis apparatus for implementing the system reliability analysis method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more system reliability analysis apparatus embodiments provided below can be found in the limitations of the system reliability analysis method described above, and will not be repeated here.

[0156] In one embodiment, such as Figure 8 As shown, a system reliability analysis device is provided, including: a construction module 801, a first determination module 802, a second determination module 803, and a third determination module 804, wherein:

[0157] Module 801 is used to construct an operator operation process model based on the basic elements of the system operation process. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0158] The first determining module 802 is used to determine the reliability quantification result of personnel resources based on the operator's operation process model;

[0159] The second determining module 803 is used to determine the failure probability of equipment resources based on the failure rate and working time of each device in the system.

[0160] The third determination module 804 is used to determine the success probability of the system's operational tasks based on the reliability quantification results and failure probability.

[0161] In one embodiment, the construction module 801 is further configured to take multiple personnel resource warehouses, multiple equipment resource warehouses and multiple operation status warehouses as a warehouse set, and determine the warehouse identifier and capacity function corresponding to each warehouse in the warehouse set; the warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate.

[0162] Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results.

[0163] Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, an operation process model for operators is constructed.

[0164] In one embodiment, the first determining module 802 is further configured to obtain the preset operation time of each operation step of the operation task through the operator operation process model; to simulate the execution of each operation step of the operation task multiple times and obtain the simulated operation time of each operation step in each simulated execution; and to calculate the probability of failure of the operator operation response based on the preset operation time of each operation step and the simulated operation time of each operation step in each simulated execution.

[0165] Based on the influence weight of the probability of personnel function failure in the system implementation environment, the probability of personnel function failure for each operation step is determined. The influence weight of the probability of personnel function failure is determined by the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the quantification results of the reliability of personnel resources.

[0166] In one embodiment, the first determining module 802 is further configured to construct an operation duration distribution function corresponding to each operation step based on all simulation operation durations of each operation step during multiple simulations.

[0167] Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining the random operation duration values ​​corresponding to each operation step based on the operation duration distribution function corresponding to each operation step.

[0168] Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success.

[0169] Count the number of times the operation response result is successful in multiple operation duration value sets, and calculate the probability of operation response failure based on the number of times.

[0170] In one embodiment, the third determining module 804 is further configured to calculate the probability of personnel operation failure for each sub-operation task based on the logical relationship of the operation steps and the probability of personnel function failure.

[0171] The success probability of an operation task is calculated based on the probability of personnel operation response failure, the probability of equipment resource failure, and the probability of personnel operation failure for each sub-operation task.

[0172] In one embodiment, the first determining module 802 is further configured to, if the logical relationship of the operation steps is a serial relationship, multiply the personnel function failure probability of each operation step in each sub-operation task, and use the result of the multiplication as the personnel operation failure probability of each sub-operation task.

[0173] If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task.

[0174] Each module in the aforementioned system reliability analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0175] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores system structure and resource data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a system reliability analysis method.

[0176] Those skilled in the art will understand that Figure 9The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0177] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0178] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0179] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0180] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0181] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0182] In one embodiment, the Petri net model includes four components: places, transitions, directed arcs, and tokens. The basic elements also include the logical relationships of operation steps and the operation states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operation states are obtained by defining multiple operation state databases, and the logical relationships of operation steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Correspondingly, when the processor executes the computer program, it also implements the following steps:

[0183] Multiple personnel resource warehouses, multiple equipment resource warehouses, and multiple operational status warehouses are treated as a warehouse set. The warehouse identifier and capacity function corresponding to each warehouse in the warehouse set are determined. The warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate.

[0184] Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results.

[0185] Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, an operation process model for operators is constructed.

[0186] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0187] By modeling the operator's operation process, the preset operation time for each step of the operation task is obtained; each step of the operation task is simulated multiple times to obtain the simulated operation time for each step in each simulation; based on the preset operation time for each step and the simulated operation time for each step in each simulation, the probability of operator response failure is calculated.

[0188] Based on the influence weight of the probability of personnel function failure in the system implementation environment, the probability of personnel function failure for each operation step is determined. The influence weight of the probability of personnel function failure is determined by the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the quantification results of the reliability of personnel resources.

[0189] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0190] Based on the total simulation operation duration of each operation step during multiple simulations, construct the operation duration distribution function corresponding to each operation step.

[0191] Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining the random operation duration values ​​corresponding to each operation step based on the operation duration distribution function corresponding to each operation step.

[0192] Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success.

[0193] Count the number of times the operation response result is successful in multiple operation duration value sets, and calculate the probability of operation response failure based on the number of times.

[0194] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0195] Based on the logical relationship of the operation steps, the probability of personnel operation failure for each sub-operation task is calculated according to the probability of personnel function failure.

[0196] The success probability of an operation task is calculated based on the probability of personnel operation response failure, the probability of equipment resource failure, and the probability of personnel operation failure for each sub-operation task.

[0197] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0198] If the logical relationship of the operation steps is serial, then the probability of personnel function failure for each operation step in each sub-operation task is multiplied together, and the result of the multiplication is taken as the probability of personnel operation failure for each sub-operation task.

[0199] If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task.

[0200] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0201] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0202] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0203] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0204] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0205] In one embodiment, the Petri net model includes four components: places, transitions, directed arcs, and tokens. The basic elements also include the logical relationships of operation steps and the operation states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operation states are obtained by defining multiple operation state databases, and the logical relationships of operation steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Correspondingly, when the computer program is executed by the processor, it also implements the following steps:

[0206] Multiple personnel resource warehouses, multiple equipment resource warehouses, and multiple operational status warehouses are treated as a warehouse set. The warehouse identifier and capacity function corresponding to each warehouse in the warehouse set are determined. The warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate.

[0207] Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results.

[0208] Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, an operation process model for operators is constructed.

[0209] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0210] By modeling the operator's operation process, the preset operation time for each step of the operation task is obtained; each step of the operation task is simulated multiple times to obtain the simulated operation time for each step in each simulation; based on the preset operation time for each step and the simulated operation time for each step in each simulation, the probability of operator response failure is calculated.

[0211] Based on the influence weight of the probability of personnel function failure in the system implementation environment, the probability of personnel function failure for each operation step is determined. The influence weight of the probability of personnel function failure is determined by the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the quantification results of the reliability of personnel resources.

[0212] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0213] Based on the total simulation operation duration of each operation step during multiple simulations, construct the operation duration distribution function corresponding to each operation step.

[0214] Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining the random operation duration values ​​corresponding to each operation step based on the operation duration distribution function corresponding to each operation step.

[0215] Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success.

[0216] Count the number of times the operation response result is successful in multiple operation duration value sets, and calculate the probability of operation response failure based on the number of times.

[0217] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0218] Based on the logical relationship of the operation steps, the probability of personnel operation failure for each sub-operation task is calculated according to the probability of personnel function failure.

[0219] The success probability of an operation task is calculated based on the probability of personnel operation response failure, the probability of equipment resource failure, and the probability of personnel operation failure for each sub-operation task.

[0220] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0221] If the logical relationship of the operation steps is serial, then the probability of personnel function failure for each operation step in each sub-operation task is multiplied together, and the result of the multiplication is taken as the probability of personnel operation failure for each sub-operation task.

[0222] If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task.

[0223] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0224] Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model.

[0225] Based on the operator's operational process model, the reliability quantification results of personnel resources are determined;

[0226] Determine the failure probability of equipment resources based on the failure rate and working duration of each device in the system;

[0227] Based on the reliability quantification results and failure probability, the success probability of the system's operational tasks is determined.

[0228] In one embodiment, the Petri net model includes four components: places, transitions, directed arcs, and tokens. The basic elements also include the logical relationships of operation steps and the operation states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operation states are obtained by defining multiple operation state databases, and the logical relationships of operation steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Correspondingly, when the computer program is executed by the processor, it also implements the following steps:

[0229] Multiple personnel resource warehouses, multiple equipment resource warehouses, and multiple operational status warehouses are treated as a warehouse set. The warehouse identifier and capacity function corresponding to each warehouse in the warehouse set are determined. The warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate.

[0230] Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results.

[0231] Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, an operation process model for operators is constructed.

[0232] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0233] By modeling the operator's operation process, the preset operation time for each step of the operation task is obtained; each step of the operation task is simulated multiple times to obtain the simulated operation time for each step in each simulation; based on the preset operation time for each step and the simulated operation time for each step in each simulation, the probability of operator response failure is calculated.

[0234] Based on the influence weight of the probability of personnel function failure in the system implementation environment, the probability of personnel function failure for each operation step is determined. The influence weight of the probability of personnel function failure is determined by the CREAM learning method. The probability of personnel operation response failure and the probability of personnel function failure are used as the quantification results of the reliability of personnel resources.

[0235] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0236] Based on the total simulation operation duration of each operation step during multiple simulations, construct the operation duration distribution function corresponding to each operation step.

[0237] Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining the random operation duration values ​​corresponding to each operation step based on the operation duration distribution function corresponding to each operation step.

[0238] Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success.

[0239] Count the number of times the operation response result is successful in multiple operation duration value sets, and calculate the probability of operation response failure based on the number of times.

[0240] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0241] Based on the logical relationship of the operation steps, the probability of personnel operation failure for each sub-operation task is calculated according to the probability of personnel function failure.

[0242] The success probability of an operation task is calculated based on the probability of personnel operation response failure, the probability of equipment resource failure, and the probability of personnel operation failure for each sub-operation task.

[0243] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0244] If the logical relationship of the operation steps is serial, then the probability of personnel function failure for each operation step in each sub-operation task is multiplied together, and the result of the multiplication is taken as the probability of personnel operation failure for each sub-operation task.

[0245] If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task is taken as the personnel operation failure probability of each sub-operation task.

[0246] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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 take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0247] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0248] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A system reliability analysis method, characterized in that, The method includes: Based on the basic elements of the system operation process, an operator operation process model is constructed. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model. Based on the operator operation process model, the reliability quantification result of the personnel resources is determined; wherein, through the operator operation process model, the preset operation time of each operation step of the operation task is obtained; each operation step of the operation task is simulated and executed multiple times to obtain the simulated operation time of each operation step in each simulation execution; based on the preset operation time of each operation step and the simulated operation time of each operation step in each simulation execution, the probability of personnel operation response failure is calculated. Based on the influence weight of the personnel function failure probability in the implementation environment of the system, the personnel function failure probability of each operation step is determined. The influence weight of the personnel function failure probability is determined by the CREAM learning method. The personnel operation response failure probability and the personnel function failure probability are used as the reliability quantification results of the personnel resources. The failure probability of each device resource is determined based on its failure rate and operating time. This is achieved by transforming the functional units and operating states of each device into a functional failure Petri net model. Based on this model, the failure probability of the device resource is calculated over a given simulation period. This functional failure Petri net model differs from the operator's operational process model. The functional failure Petri net models constructed based on the different functional units of different devices are categorized into warm backup dual redundancy structures and hot backup dual redundancy structures. Based on the reliability quantification results and the failure probability, the success probability of the system's operation task is determined.

2. The method according to claim 1, characterized in that, The Petri net model comprises four elements: places, transitions, directed arcs, and tokens. These basic elements also include logical relationships between operation steps and operation states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operation states are obtained by defining multiple operation state databases, and logical relationships between operation steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Accordingly, the construction of the operator operation process model based on the basic elements of the system operation process includes: The plurality of personnel resource warehouses, the plurality of equipment resource warehouses, and the plurality of operation status warehouses are taken as a warehouse set, and the warehouse identifier and capacity function corresponding to each warehouse in the warehouse set are determined; the warehouse identifier is used to uniquely identify the warehouse, and the capacity function is used to calculate the number of tokens that the warehouse can accommodate. Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results. Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, the operation process model of the operator is constructed.

3. The method according to claim 1, characterized in that, The duration of all simulated operations for each operation step in multiple simulations follows a normal distribution; the statistical calculation of the probability of operator response failure based on the preset operation duration of each operation step and the simulated operation duration of each operation step in each simulation includes: Based on the total simulation operation duration of each operation step during multiple simulations, construct the operation duration distribution function corresponding to each operation step. Construct multiple sets of operation duration values; each set of operation duration values ​​is obtained by combining a random operation duration value corresponding to each operation step based on the operation duration distribution function corresponding to each operation step. Based on the preset operation time for each operation step, determine the operation response result corresponding to each set of operation time values; the operation response result is either failure or success. The number of times the operation response result is successful in the set of multiple operation duration values ​​is counted, and the probability of the operation response failure of the personnel is calculated based on the number of times.

4. The method according to claim 2 or 3, characterized in that, The operation task is composed of multiple sub-operation tasks, and all operation steps of the operation task are respectively configured in the multiple sub-operation tasks. Determining the success probability of the system's operational tasks based on the reliability quantification result and the failure probability includes: Based on the logical relationship of the operation steps, and according to the personnel function failure probability, the personnel operation failure probability of each sub-operation task is calculated. The success probability of the operation task is calculated based on the failure probability of the personnel operation response, the failure probability of the equipment resources, and the failure probability of the personnel operation for each sub-operation task.

5. The method according to claim 4, characterized in that, The calculation of the personnel operation failure probability for each sub-operation task based on the logical relationship of the operation steps and the personnel function failure probability includes: If the logical relationship of the operation steps is a serial relationship, then the personnel function failure probability of each operation step in each sub-operation task is multiplied together, and the result of the multiplication is taken as the personnel operation failure probability of each sub-operation task. If the logical relationship of the operation steps is parallel, then the maximum value of the personnel function failure probability of each operation step in each sub-operation task shall be taken as the personnel operation failure probability of each sub-operation task.

6. A system reliability analysis device, characterized in that, The device includes: The construction module is used to construct an operator operation process model based on the basic elements of the system operation process. The basic elements include operation resources, which include personnel resources and equipment resources. The basic elements are obtained by defining the system operation process based on the Petri net model. The first determining module is used to determine the reliability quantification result of the personnel resources based on the operator operation process model; wherein, through the operator operation process model, the preset operation time of each operation step of the operation task is obtained; each operation step of the operation task is simulated and executed multiple times to obtain the simulated operation time of each operation step in each simulation execution; based on the preset operation time of each operation step and the simulated operation time of each operation step in each simulation execution, the probability of personnel operation response failure is calculated. Based on the influence weight of the personnel function failure probability in the implementation environment of the system, the personnel function failure probability of each operation step is determined. The influence weight of the personnel function failure probability is determined by the CREAM learning method. The personnel operation response failure probability and the personnel function failure probability are used as the reliability quantification results of the personnel resources. The second determining module is used to determine the failure probability of the equipment resources based on the failure rate and working duration of each device in the system. This is achieved by converting the functional units and working states of each device into a functional failure Petri net model. Based on this model, the failure probability of the equipment resources is calculated over a given simulation time. The functional failure Petri net model differs from the operator's operational process model. The functional failure Petri net models constructed based on the different functional units of different devices are divided into warm backup dual redundancy structures and hot backup dual redundancy structures. The third determining module is used to determine the success probability of the system's operation task based on the reliability quantification result and the failure probability.

7. The apparatus according to claim 6, characterized in that, The Petri net model comprises four elements: places, transitions, directed arcs, and tokens. These basic elements also include the logical relationships of operational steps and operational states. Personnel resources are obtained by defining multiple personnel resource databases, equipment resources are obtained by defining multiple equipment resource databases, operational states are obtained by defining multiple operational state databases, and the logical relationships of operational steps are obtained by defining directed arcs between at least two of the multiple personnel resource databases and multiple equipment resource databases. Correspondingly, the construction module is also used to treat the multiple personnel resource databases, the multiple equipment resource databases, and the multiple operational state databases as a set of places, and to determine the place identifier and capacity function corresponding to each place in the set. The location identifier is used to uniquely identify a location, and the capacity function is used to calculate the number of tokens that a location can hold. Determine the transition data set of the adjacent directed arcs for each storage location, and determine the weight function of the adjacent directed arcs for each storage location based on the storage location classification results. Based on the set of storage locations, the set of transition data, the set of directed arcs, the classification results of storage locations, the identification of storage locations, the capacity function, the weight function, and the logical relationship of the operation steps, the operation process model of the operator is constructed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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