Multi-stage task system reliability redundancy allocation method, device and computer equipment with stage backup

Abstract

The application relates to a multi-stage task system reliability redundancy allocation method, device and computer equipment with stage backup, which comprises the following steps: constructing a reliability redundancy allocation model according to the total constraint condition of system components and the constraint condition of different stage components of the system, taking the maximization of system task reliability as a target; determining an approximate feasible path set and the number of feasible paths of the system according to the working component information of each stage, the stage backup scheme and the pre-device stage scheduling strategy; determining the system path set weighted value under a preset allocation scheme according to the approximate feasible path set of the system and the reliability level of the feasible paths; taking the system path set weighted value as a heuristic factor to determine the initial feasible allocation scheme solution set; and solving and calculating the reliability redundancy allocation model by using a genetic algorithm to obtain the reliability redundancy allocation scheme. The method can quickly obtain a feasible redundancy allocation scheme and improve the efficiency of the reliability redundancy allocation of the multi-stage task system.

Description

Technical Field

[0001] This application relates to the field of system reliability technology, and in particular to a method, apparatus and computer equipment for redundancy allocation of reliability in a multi-stage task system with phased backup. Background Technology

[0002] Reliability allocation is a crucial task in system design, aiming to ensure that the system's reliability meets the required level. The Reliability Allocation Problem (RAP) has yielded significant research results, with main methods broadly categorized into three types: heuristic algorithms, metaheuristic algorithms, and exact optimization methods. However, when the problem is large-scale, obtaining an exact solution is difficult. Therefore, metaheuristic algorithms are often used in such cases, including genetic algorithms, artificial bee colony optimization, bat algorithm particle swarm optimization, Tabu search, Jaya algorithm, artificial immune system, cuckoo search algorithm, harmony search algorithm, bee search, simplified swarm optimization, and combinations of these algorithms. In recent years, RAP research has focused on two main areas: 1) providing new redundancy strategies; and 2) providing methods that consider complex situations such as common-cause failures and multi-state systems.

[0003] With the continuous innovation and advancement of science and technology, mission systems in the engineering field are typically composed of large quantum systems and devices distributed in different locations. These different subsystems have relatively independent physical structures and functions. Examples include space telemetry and communications (TT&C) systems and high-speed rail systems. In these mission systems, the entire system mission can be divided into continuous, non-overlapping dependent phases. Different phases use different combinations of components and different success criteria to perform different phase tasks; systems with these attributes are called PMS (Personalized Management Systems). For example, the mission of a TT&C system can be divided into four phases: launch preparation phase, ascent phase, orbit insertion phase, and on-orbit servicing phase. TT&C stations at different locations provide services to the spacecraft within different time windows. The time windows of different TT&C stations may differ, therefore, the TT&C equipment for the same spacecraft will change during a given mission period. Furthermore, different phases require different tasks. For example, rocket orbit tracking should be performed during the launch phase, while spacecraft attitude control and data transmission occur simultaneously during the ascent and orbit insertion phases; therefore, the success criteria for different phases are different.

[0004] The mission reliability of a PMS (Plan-Do-Check-Act) system is defined as the probability of successfully completing the system mission within a given mission profile. Zhang Xingui et al. transformed the mission reliability allocation problem of aerospace telemetry and control systems into a constrained combinatorial optimization problem, proposing an adaptive particle swarm optimization algorithm and an adaptive hybrid learning algorithm based on RBFNN. Rui Peng et al. constructed a PMS reliability redundancy allocation model considering two failure modes (internal failure and failure caused by external influences), with the optimization objective of maximizing system reliability. Xiang-Yu Li et al. addressed the redundancy allocation problem of PMS components following a non-exponential distribution and having both warm and cold backup modes, constructing an allocation model with maximizing system reliability as the objective function, and solved the model using an improved genetic algorithm.

[0005] Existing research has achieved some results in the field of reliability allocation, but it has not studied the RAP problem of multi-stage task systems with staged backup, while staged backup is common in practical engineering systems. Summary of the Invention

[0006] Therefore, it is necessary to provide a method, apparatus, and computer equipment for redundancy allocation of reliability in a multi-stage task system with staged backup, in order to address the above-mentioned technical problems.

[0007] A method for reliability redundancy allocation in a multi-stage task system with staged backup, the method comprising:

[0008] Based on the component constraints at different stages of the system and the overall constraints of the system components, a reliability redundancy allocation model for a multi-stage task system is constructed with the optimization objective of maximizing the reliability of the system task.

[0009] Based on the working component information of each stage in a multi-stage task system with stage backup, the stage backup scheme, and the preset backup stage scheduling strategy, an approximate feasible path set and the number of feasible paths of the system are calculated using a path number approximation algorithm.

[0010] Based on the approximate feasible path set and the reliability level of the feasible path of the system, the weighted value of the system path set under the preset allocation scheme is determined.

[0011] Using the weighted values ​​of the system path set as heuristic factors, a set of initial feasible allocation scheme solutions is determined and used as the initial feasible solution set for the genetic algorithm.

[0012] A genetic algorithm is used to solve the reliability redundancy allocation model to obtain a reliability redundancy allocation scheme for a multi-stage task system with stage backup.

[0013] In one embodiment, based on the working component information of each stage in a multi-stage task system with staged backup, the stage backup scheme, and the preset backup stage scheduling strategy, an approximate feasible path set and the number of feasible paths of the system are calculated using a path number approximation algorithm, including:

[0014] Based on the working component information, stage backup plan, and preset backup stage scheduling strategy for each stage, determine the initial set of feasible paths for each stage.

[0015] The initial feasible path set of the initial stage is used as the current path set. The stage task set after adding the current stage task under each path is calculated. The path set of the current stage under each path is updated. After deleting the paths in the path set of the current stage where the total number of failed tasks is greater than a preset value or the number of failed tasks of a certain task exceeds a preset value, the remaining paths in the path set of the current stage under each path are used as the current path set. The subsequent stages are processed until all stages are traversed, and the total path set of the system is obtained.

[0016] Traverse the total path set of the system, delete paths for which the phase tasks have not yet been completed, and obtain the approximate feasible path set and the number of feasible paths of the system under a certain allocation scheme.

[0017] In one embodiment, based on the approximate feasible path set and the reliability level of feasible paths in the system, a weighted value for the system path set under a preset allocation scheme is determined, including:

[0018] The failure indication functions for each working component are constructed as follows:

[0019]

[0020] Where, f(h) i ) is the failure indication function for the working component; h i For the i-th stage in the system pathway; F(h) i (This refers to stage h without considering component dependencies between stages) i The task is inefficient.

[0021] Based on the failure indication function of each working component in the system path of the approximate feasible path set of the system, the weighted path value based on the failure rate of each system path is obtained as follows:

[0022]

[0023] Among them, w(PT) i ) represents the weighted path value of the i-th system path based on the failure rate at the failure stage, PT i For the i-th system pathway, f(h) iLet h be the failure indication function for the i-th working component in the system path. i Let be the i-th stage in the system pathway, and h be the total number of working stages in the system pathway.

[0024] Based on the weighted path value of each system path according to the failure rate at each failure stage, the weighted value of the system path set under the current allocation scheme is obtained as follows:

[0025]

[0026] Where w(PT) is the weighted value of the system path set, and PN is the total number of system paths.

[0027] In one embodiment, the set of initial feasible allocation solutions is determined using the weighted value w(PT) of the system path set as a heuristic factor, including:

[0028] At the start of the allocation, there is no path in the system. Add a set of equipment to any subsystem of the multi-stage task system; calculate the change in the number of failed stage tasks in the system after adding a set of equipment; select the maximum value of the change in the number of failed stage tasks for allocation; if there are multiple stages with the same change in the number of failed stage tasks, allocate according to the principle of random allocation to obtain a set of initial feasible allocation solutions.

[0029] Given that a system path already exists, calculate the change in the weighted value of the system path set after adding redundant devices to different subsystems; select the system path set with the largest change in weighted value for redundant device allocation; if multiple system path sets have the same change in weighted value, allocate according to the random allocation principle to obtain a set of initial feasible allocation solutions.

[0030] In one embodiment, based on the constraints of components at different stages of the system and the total constraints of the system components, and with the optimization objective of maximizing the reliability of the system task, a reliability redundancy allocation model for a multi-stage task system is constructed as follows:

[0031] maxR(n1,n2,…,n s )

[0032]

[0033]

[0034] Where (n1,n2,…,n) s Let n be an allocation scheme. i Let be the number of component redundancies in the i-th subsystem, s be the total number of sub-coefficients, and R(n1,n2,…,n) be the total number of sub-coefficients. s The system task reliability under this allocation scheme is denoted as . Let N be the upper limit of the number of redundant components in the i-th subsystem, and let N be the upper limit of the system's constraint on the redundant data of components.

[0035] In one embodiment, a genetic algorithm is used to solve the reliability redundancy allocation model to obtain a reliability redundancy allocation scheme for a multi-stage task system with staged backups, including:

[0036] Use the initial set of feasible solutions from the genetic algorithm as the initial population.

[0037] The initial population is further optimized by substituting it into a genetic algorithm. Through selection, crossover, and mutation, the population individuals are updated to obtain the optimal solution for the reliability redundancy allocation scheme of a multi-stage task system with stage backup.

[0038] In one embodiment, the preset backup phase scheduling strategy includes:

[0039] The failed phase task will be redundantly executed in the nearest backup phase.

[0040] In cases of redundancy and conflict, tasks that occur earlier in the initial stage have higher priority.

[0041] When performing redundancy during the backup phase, only one component is assigned to each redundancy task for the failure phase, while the remaining components perform the initial tasks of the current phase.

[0042] A reliability redundancy allocation device for a multi-stage task system with staged backup, the device comprising:

[0043] The reliability redundancy allocation modeling module is used to construct a reliability redundancy allocation model for a multi-stage task system based on the component constraints at different stages of the system and the total constraints of the system components, with the optimization objective of maximizing the reliability of the system task.

[0044] The system approximate feasible path set calculation module is used to calculate the approximate feasible path set and the number of feasible paths of the system based on the working component information of each stage in a multi-stage task system with stage backup, the stage backup scheme, and the preset backup stage scheduling strategy, using a path number approximation algorithm.

[0045] The reliability redundancy allocation scheme determination module is used to determine the weighted value of the system path set under the preset allocation scheme based on the approximate feasible path set and the reliability level of the feasible path of the system; to determine the set of initial feasible allocation scheme solutions using the weighted value of the system path set as the heuristic factor, and to use it as the initial feasible solution set of the genetic algorithm; and to use the genetic algorithm to solve the reliability redundancy allocation model to obtain the reliability redundancy allocation scheme of the multi-stage task system with stage backup.

[0046] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of any of the methods described above.

[0047] The aforementioned method, apparatus, and computer equipment for reliability redundancy allocation in a multi-stage task system with phased backups include: constructing a reliability redundancy allocation model for the multi-stage task system based on the constraints of components at different stages of the system and the total constraints of the system components, with the optimization objective of maximizing system task reliability; calculating the approximate feasible path set and the number of feasible paths of the system using a path number approximation algorithm based on the working component information, phased backup scheme, and preset backup phase scheduling strategy of each stage in the multi-stage task system with phased backups; determining the system path set weighting value under the preset allocation scheme based on the system's approximate feasible path set and the reliability level of the feasible paths; determining the initial feasible allocation scheme solution set using the system path set weighting value as a heuristic factor, and using it as the initial feasible solution set for a genetic algorithm; and solving the reliability redundancy allocation model using a genetic algorithm to obtain the reliability redundancy allocation scheme for the multi-stage task system with phased backups. This method can quickly obtain a feasible redundancy allocation scheme, improving the efficiency of reliability redundancy allocation in multi-stage task systems. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a reliability redundancy allocation method for a multi-stage task system with staged backup in one embodiment.

[0049] Figure 2 Here is a block diagram of the reliability of a 3-stage task in another embodiment;

[0050] Figure 3 This is a schematic diagram of the algorithm for approximating the number of paths in another embodiment;

[0051] Figure 4 This is a schematic diagram of the redundancy allocation algorithm in another embodiment;

[0052] Figure 5 This is a schematic diagram of the hybrid metaheuristic algorithm flow in another embodiment.

[0053] Figure 6 This is a schematic diagram illustrating the phase division of the measurement and control task in another embodiment;

[0054] Figure 7 This is a structural block diagram of a reliability redundancy allocation device for a multi-stage task system with staged backup in one embodiment.

[0055] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] 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.

[0057] In one embodiment, such as Figure 1 As shown, a method for reliability redundancy allocation in a multi-stage task system with staged backup is provided. The method includes the following steps:

[0058] Step 100: Based on the component constraints at different stages of the system and the overall constraints of the system components, construct a reliability redundancy allocation model for the multi-stage task system with the optimization objective of maximizing system task reliability.

[0059] Specifically, since a stage may have multiple backup stages, and a backup stage can execute multiple failed tasks, under a given redundancy allocation scheme, different backup stage scheduling strategies will still result in different system task reliability under the same redundancy allocation. Therefore, the backup stage scheduling strategy selected in this application is to serve redundant execution stages according to time priority, that is, the first-come-first-served strategy, and the redundancy selection rule is to always select the backup stage closest to the time of the failed task as the task re-execution stage.

[0060] Under a defined backup phase scheduling strategy and reliability allocation scheme, the system's task execution sequence will still dynamically change with the state of components. The system's task reliability varies under different task execution sequences. Since aerospace telemetry and control communication systems have extremely high requirements for task reliability, maximizing the actual task reliability of the system is usually the primary objective. Furthermore, different telemetry and control stations have different locations, so it is necessary to consider both the limitations on the number of spatially redundant components at each station and the constraints on the total number of all redundant components.

[0061] Assuming that the reliability of a component follows an exponential distribution, its working state at a certain point in time is a random variable. The system task state is a function of the component task state, so the system task state is also a random variable. We can calculate the probability of the task state being at a successful moment.

[0062] Step 102: Based on the working component information of each stage in a multi-stage task system with stage backup, the stage backup scheme, and the preset backup stage scheduling strategy, the approximate feasible path set and the number of feasible paths of the system are calculated using a path number approximation algorithm.

[0063] Specifically, a multi-stage mission system with phased backups can be, but is not limited to, aerospace telemetry and control systems.

[0064] As a preferred option, the backup phase scheduling strategy is to serve the redundant execution phases according to time priority, i.e., the first-come, first-served strategy. The redundancy selection rule is to always select the backup phase closest to the time of the failed task as the task re-execution phase.

[0065] This application employs a total probability approach when calculating the reliability of a multi-stage task system with backup phases. The system reliability is ultimately obtained by calculating all feasible paths in the system, the probability of each path's occurrence, and the path's reliability. In a multi-stage task system with phased backup characteristics, the approximate feasible paths and their number are dynamic and change with different redundancy allocation schemes (the number of devices in different phases).

[0066] The path number approximation algorithm is a path number approximation algorithm that considers the contribution of path reliability. In this algorithm, only paths with a high path reliability contribution rate are calculated, while some system paths with a low path reliability contribution rate are ignored.

[0067] Furthermore, if the stages of a system are independent, the number of system paths equals the product of the number of paths in each stage. However, in a multi-stage task system, the stages are not independent, and their states may be mutually exclusive. For example, a component from a preceding stage may be reused in a subsequent stage. Therefore, the failure of a preceding stage will affect whether the subsequent stage can execute successfully or become a redundant backup stage. Consequently, the number of system paths is generally less than the number of paths in each stage. In the multi-stage task system described in this application, the components in each stage are irreparable during the task; therefore, once a component fails, it no longer participates in the execution of the system task. To reduce path solving time, a method of simplification and solution is adopted here.

[0068] Step 104: Based on the approximate feasible path set and the reliability level of feasible paths in the system, determine the weighted value of the system path set under the preset allocation scheme.

[0069] Specifically, due to the high reliability of components, the probability of failure in a stage is very small. The more failure stages a system-level path contains, the lower the task reliability of that path. The smaller the incremental reliability of the system task using this redundancy scheme, the more important it is to consider both the number of system paths and the number of failure stages in different paths when selecting different component scheduling methods with the goal of maximizing task reliability.

[0070] Step 106: Using the weighted values ​​of the system path set as heuristic factors, determine the set of initial feasible allocation scheme solutions, and use it as the initial feasible solution set for the genetic algorithm.

[0071] A feasible redundancy allocation scheme can be quickly obtained through heuristic algorithms. Here, heuristic algorithms are combined with genetic algorithms to further optimize the algorithm.

[0072] Different allocation schemes, under the same backup phase scheduling strategy, will also generate different system paths. The paths generated in this case are due to the design that phase tasks can be backed up, and do not affect the system reliability in the absence of redundancy design. In addition, the task reliability of each path is positive. Generally, the more feasible paths the system has under different redundancy allocation schemes, the higher the system reliability. Therefore, the weighted value of the system path set can be used as a heuristic factor for reliability allocation.

[0073] Step 108: Use a genetic algorithm to solve the reliability redundancy allocation model to obtain a reliability redundancy allocation scheme for a multi-stage task system with stage backup.

[0074] The aforementioned method for reliability redundancy allocation in a multi-stage task system with phased backup includes: constructing a reliability redundancy allocation model for the multi-stage task system based on the constraints of components at different stages of the system and the total constraints of the system components, with the optimization objective of maximizing system task reliability; calculating the approximate feasible path set and the number of feasible paths of the system using a path number approximation algorithm based on the working component information, phased backup scheme, and preset backup phase scheduling strategy of each stage in the multi-stage task system with phased backup; determining the system path set weighting value under the preset allocation scheme based on the system's approximate feasible path set and the reliability level of the feasible paths; determining the initial feasible allocation scheme solution set using the system path set weighting value as a heuristic factor, and using it as the initial feasible solution set for the genetic algorithm; and solving the reliability redundancy allocation model using the genetic algorithm to obtain the reliability redundancy allocation scheme for the multi-stage task system with phased backup. This method can quickly obtain a feasible redundancy allocation scheme, improving the efficiency of reliability redundancy allocation in multi-stage task systems.

[0075] In one embodiment, step 102 includes: determining the initial feasible path set for each stage based on the working component information of each stage, the stage backup scheme, and the preset backup stage scheduling strategy; using the initial feasible path set of the initial stage as the current path set, calculating the stage task set after adding the current stage task under each path, updating the path set of the current stage under each path, deleting paths in the path set of the current stage where the total number of task failures is greater than a preset value or the number of failures of a certain task exceeds a preset value, using the remaining paths in the path set of the current stage under each path as the current path set, and continuing to process subsequent stages until all stages have been traversed to obtain the total path set of the system; traversing the total path set of the system, deleting paths where stage tasks have not yet been completed, to obtain the approximate feasible path set and the number of feasible paths of the system under a certain allocation scheme.

[0076] Specifically, the following is a brief explanation of how to solve for an approximate feasible path in a system with two telemetry and control stations and three phases (a, b, and c). Its reliability block diagram is shown below. Figure 2 As shown.

[0077] The system has a total of 3 sets of telemetry and control equipment. Since each telemetry and control station needs at least one set of equipment to complete the current telemetry and control task, there are two allocation schemes: 1) Telemetry and control station a is allocated two sets of equipment, and telemetry and control station b is allocated one set of equipment; 2) Telemetry and control station b is allocated two sets of equipment, and telemetry and control station a is allocated one set of equipment. Assume that the telemetry and control time for each stage is the same, t, and that the failure rate of the telemetry and control equipment follows an exponential distribution with parameter λ, assuming r = e^(-λ / t). -λt When there is no stage backup between stages, both of the above allocation schemes have only one stage-level path, namely: h1→h2→h3.

[0078] If the system has phased backups, assuming a task that failed in phase 1 can be executed in phase 2, then the system path will no longer be limited to one. In addition to the path mentioned above, there will also be a path h1′→h1′h2→h3. The path h1′→h1′h2→h3 indicates that the task failed in phase 1 and is being repeated in phase 2. This repetition requires available equipment in phase 2. Allocation scheme 1 failed in phase 1, meaning both sets of equipment at site a failed, while site b only has one set of equipment. Therefore, under allocation scheme 1, this same path cannot exist.

[0079] This case demonstrates that different allocation schemes, under the same backup phase scheduling strategy, can generate different system-level paths. These paths arise because of the design that phase tasks can be backed up, and do not affect system reliability without redundancy. Furthermore, the reliability of each path is positive. Generally, the more feasible paths the system has under different redundancy allocation schemes, the higher the system reliability. Therefore, a weighted average of the system path set can be used as a heuristic factor for reliability allocation.

[0080] To use the weighted value of the system path set as a heuristic factor, the problem of calculating the number of system paths must first be solved. Since the number of system paths still depends on the scheduling strategy under a given allocation scheme, this embodiment mainly studies the redundancy allocation problem under a determined scheduling strategy. Preferably, the following backup phase scheduling strategy is selected:

[0081] (1) Failed phase tasks will be redundantly performed in the nearest backup phase;

[0082] (2) In case of redundancy and conflict, the task that comes earlier in the initial stage has higher priority;

[0083] (3) When performing redundancy during the backup phase, only one component is allocated to each redundancy task, and the remaining components (at least 1) are used to perform the initial task of the current phase.

[0084] In the case of phased dynamic backup, assuming that the number of backup phases in a phase is n. i So at most, this stage can have 100,000. There are multiple paths. Therefore, when a system has a large number of stages, and each stage has a large number of redundant tasks, the calculated number of system paths will be extremely large.

[0085] However, in this embodiment, the number of system-level paths is a heuristic factor, so an exact solution is usually not required. Furthermore, paths with extremely low reliability contribute little to improving system reliability. Therefore, an approximate solution algorithm for the number of paths considering their reliability contribution is proposed. In this algorithm, only paths with a high reliability contribution rate are calculated, while some system paths with a low reliability contribution rate are ignored. The rule of this solution algorithm is as follows: a system path is no longer considered when it falls under the following conditions:

[0086] (1) At a certain moment in the system, there are more than k (excluding k) failed tasks at the same time;

[0087] (2) A certain task fails more than k times.

[0088] Where k is an integer, and its calculation method is as follows:

[0089] max k

[0090] st(1-R(h low )) k ≤M

[0091] Where R(h) low ) is the lowest stage reliability value for the task, and M is the required level of task reliability accuracy for the system.

[0092] Furthermore, if the stages of a system are independent, the number of system paths equals the product of the number of paths in each stage. However, in a multi-stage task system, the stages are not independent, and their states may be mutually exclusive. For example, a component from a previous stage may be reused in a subsequent stage. Therefore, the failure of a previous stage will affect whether the subsequent stage can execute successfully or become a redundant backup stage. Thus, the number of system paths is generally less than the number of paths in each stage. During the task, components are unrepairable; therefore, once a component fails, it no longer participates in the execution of the system task. To reduce path calculation time, a method of simplification and calculation is adopted. The algorithm for approximating the number of paths is as follows: Figure 3 As shown.

[0093] First, determine the redundancy allocation scheme. Under the determined redundancy scheme and backup phase scheduling strategy, set the initial path set for each phase as T. i (U), and record the number of times the current task fails and the total number of failures in the current path. If the number of failures is greater than k, the path is removed.

[0094] In one embodiment, step 104 includes: constructing a failure indication function for each working component as follows:

[0095]

[0096] Where, f(h) i ) is the failure indication function for the working component; h i For the i-th stage in the system pathway; F(h) i (This refers to stage h without considering component dependencies between stages) i The task is inefficient.

[0097] Based on the failure indication function of each working component in the system path within the approximate feasible path set, the weighted path value based on the failure rate at each failure stage is obtained for each system path:

[0098]

[0099] Among them, w(PT) i ) represents the weighted path value of the i-th system path based on the failure rate at the failure stage, PTi For the i-th system pathway, f(h) i Let h be the failure indication function for the i-th working component in the system path. i Let be the i-th stage in the system pathway, and h be the total number of working stages in the system pathway.

[0100] Based on the weighted path value of each system path according to the failure rate at each failure stage, the weighted value of the system path set under the current allocation scheme is obtained as follows:

[0101]

[0102] Where w(PT) is the weighted value of the system path set, and PN is the total number of system paths.

[0103] Specifically, due to the high reliability of components, the probability of failure in a stage is very small. The more failure stages a system-level path contains, the lower the task reliability of that path. The smaller the incremental reliability of the system task using this redundancy scheme, the more important it is to consider both the number of system paths and the number of failure stages in different paths when selecting different component scheduling methods with the goal of maximizing task reliability.

[0104] Assuming that component dependencies between stages are not considered, the task failure rate for each stage can be calculated as F(h). i Here, the failure indication function f(h) is defined. i If h i In the failure phase of the system pathway, f(h) i )=F(h i Otherwise, f(h) i ) = 1, based on this function, for system path PT i The weighted path value based on the failure rate of the failure stage is shown in Equation (5).

[0105] The weighted values ​​of the system path set under this allocation scheme are shown in formula (6).

[0106] In one embodiment, step 106 includes: at the start of allocation, when there is no path in the system, adding a set of devices to any subsystem of the multi-stage task system; calculating the change in the number of failed stage tasks in the system after adding a set of devices; selecting the maximum value of the change in the number of failed stage tasks for allocation; if there are multiple instances of equal change in the number of failed stage tasks, allocation is performed according to the principle of random allocation to obtain a set of initial feasible allocation solutions; when there is already a system path, calculating the change in the weighted value of the system path set after adding redundant devices to different subsystems; selecting the maximum value of the change in the weighted value of the system path set for redundant device allocation; if there are multiple instances of equal change in the weighted value of the system path set, allocation is performed according to the principle of random allocation to obtain a set of initial feasible allocation solutions.

[0107] Specifically, when performing redundancy allocation, it is necessary to calculate the change in the weighted path weights of the system after adding one set of redundant equipment to a certain telemetry and control station, i.e., Δw(PT|s i )=w(PT|s i )-w(PT), select Δw(PT|s i The maximum value is allocated; if multiple Δw(PT|s) exist... i If the numbers are equal, then allocation is performed according to the random allocation principle. Assume a telemetry and control communication system has h tasks in stages, s telemetry and control stations, and N sets of telemetry and control equipment. Each telemetry and control station has at most N... i + The redundancy allocation algorithm flow for a set of measurement and control equipment is as follows: Figure 4 As shown, the allocation scheme can be obtained quickly.

[0108] In the above allocation process, since there are no pathways in the system at the beginning, the calculation required is to determine the change in the number of failed phase tasks in the system after adding one set of equipment to a certain telemetry and control station, assuming all components remain intact. Δta|s i =ta|s i -ta, select Δta|s i The maximum value is allocated; if multiple Δta|s exist... i If they are equal, then they will be allocated according to the principle of random allocation.

[0109] In one embodiment, step 100 includes: based on the constraints of components at different stages of the system and the total constraints of the system components, and with maximizing the reliability of the system task as the optimization objective, constructing a reliability redundancy allocation model for the multi-stage task system as follows:

[0110] maxR(n1,n2,…,n s (7)

[0111]

[0112] Where (n1,n2,…,n) s Let n be an allocation scheme. i Let be the number of component redundancies in the i-th subsystem, s be the total number of sub-coefficients, and R(n1,n2,…,n) be the total number of sub-coefficients. s The system task reliability under this allocation scheme is denoted as . Let N be the upper limit of the number of redundant components in the i-th subsystem, and let N be the upper limit of the system's constraint on the redundant data of components.

[0113] In one embodiment, step 108 includes: using the initial feasible solution set of the genetic algorithm as the initial population; substituting the initial population into the genetic algorithm for further optimization; updating the population individuals through selection, crossover, and mutation to obtain the optimal solution of the reliability redundancy allocation scheme of the multi-stage task system with stage backup.

[0114] Specifically, a feasible redundancy allocation scheme can be quickly obtained through heuristic algorithms. Here, the heuristic algorithm is combined with the genetic algorithm to further optimize the algorithm. The hybrid heuristic algorithm flow is as follows: Figure 5 As shown. In the process of the hybrid meta-heuristic algorithm (an algorithm that combines heuristic algorithm and genetic algorithm, namely the algorithm of this application), a series of better feasible solutions are first obtained quickly through heuristic algorithm, and this series of solution sets are used as the initial population and substituted into the genetic algorithm for further optimization. Through selection, crossover and mutation, the population individuals are updated to obtain a better allocation scheme.

[0115] In one embodiment, the failed stage task will perform redundancy in the nearest backup stage; in the event of redundancy conflict, the task that comes earlier in the initial stage has higher priority; when performing redundancy in the backup stage, each redundant task is assigned only one component to the failed stage, and the remaining components are used to perform the initial task of the current stage.

[0116] It should be understood that, although Figure 1 , Figures 3 to 5 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 , Figures 3 to 5At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0117] In a verification embodiment, a space tracking and control mission with one tracking and control center and five tracking and control stations is used as an example to illustrate the heuristic allocation algorithm based on the number of weighted paths. Assume that in this launch mission, there are a total of eight sets of tracking and control equipment, and each tracking and control station can accommodate a maximum of three tracking and control devices. The tracking and control mission can be divided into seven phases based on the visible time windows of the tracking and control stations, as shown in the diagram. Figure 6 As shown.

[0118] To verify the efficiency of the algorithm, an initial population of 10 was used, and the hybrid metaheuristic algorithm was compared with a general genetic algorithm. The calculation results are shown in Table 1.

[0119] Table 1 Calculation Results

[0120] Hybrid metaheuristic algorithms 1 0.9921135 30203 Genetic Algorithm 1 0.9914862 22103 Hybrid metaheuristic algorithms 2 0.9921436 20303 Genetic Algorithm 2 0.9916594 21113 Hybrid metaheuristic algorithms 3 0.9921436 20303 Genetic Algorithm 3 0.9916594 21113 Hybrid metaheuristic algorithms 4 0.9921436 20303 Genetic Algorithm 4 0.9921135 30203 Hybrid metaheuristic algorithms 5 0.9921436 20303 Genetic Algorithm 5 0.9921125 30203

[0121] In one embodiment, such as Figure 7 As shown, a reliability redundancy allocation device for a multi-stage task system with phased backup is provided, comprising: a reliability redundancy allocation modeling module, a system approximate feasible path set calculation module, and a reliability redundancy allocation scheme determination module, wherein:

[0122] The reliability redundancy allocation modeling module is used to construct a reliability redundancy allocation model for a multi-stage task system based on the component constraints at different stages of the system and the total constraints of the system components, with the optimization objective of maximizing the reliability of the system task.

[0123] The system approximate feasible path set calculation module is used to calculate the approximate feasible path set and the number of feasible paths of the system based on the working component information of each stage in a multi-stage task system with stage backup, the stage backup scheme, and the preset backup stage scheduling strategy, using a path number approximation algorithm.

[0124] The reliability redundancy allocation scheme determination module is used to determine the weighted value of the system path set under the preset allocation scheme based on the approximate feasible path set and the reliability level of the feasible path; using the weighted value of the system path set as a heuristic factor, it determines the set of initial feasible allocation scheme solutions and uses it as the initial feasible solution set of the genetic algorithm; the genetic algorithm is used to solve the reliability redundancy allocation model to obtain the reliability redundancy allocation scheme of the multi-stage task system with stage backup.

[0125] In one embodiment, the system approximate feasible path set calculation module is further configured to determine the initial feasible path set for each stage based on the working component information of each stage, the stage backup scheme, and the preset backup stage scheduling strategy; use the initial feasible path set of the initial stage as the current path set; calculate the stage task set after adding the current stage task under each path; update the path set of the current stage under each path; delete paths in the path set of the current stage where the total number of task failures is greater than a preset value or the number of failures of a certain task exceeds a preset value; use the remaining paths in the path set of the current stage under each path as the current path set; continue to process subsequent stages until all stages have been traversed to obtain the total system path set; traverse the total system path set; delete paths where stage tasks have not yet been completed; and obtain the approximate feasible path set and the number of feasible paths of the system under a certain allocation scheme.

[0126] In one embodiment, the reliability redundancy allocation scheme determination module is also used to construct the failure indication function of each working component as shown in formula (4); based on the failure indication function of each working component in the system path in the approximate feasible path set of the system, the weighted path value based on the failure rate of each system path is obtained as shown in formula (5); based on the weighted path value based on the failure rate of each system path, the weighted value of the system path set under the current allocation scheme is obtained as shown in formula (6).

[0127] In one embodiment, the reliability redundancy allocation scheme determination module is further configured to: at the start of allocation, when there is no path in the system, add a set of equipment to any subsystem of the multi-stage task system; calculate the change in the number of failed stage tasks in the system after adding a set of equipment; select the maximum value of the change in the number of failed stage tasks for allocation; if there are multiple changes in the number of failed stage tasks equal, then allocate according to the principle of random allocation to obtain a set of initial feasible allocation scheme solutions; when there is already a system path, calculate the change in the weighted value of the system path set after adding redundant equipment to different subsystems; select the maximum value of the change in the weighted value of the system path set for redundant equipment allocation; if there are multiple changes in the weighted value of the system path set equal, then allocate according to the principle of random allocation to obtain a set of initial feasible allocation scheme solutions.

[0128] In one embodiment, the reliability redundancy allocation model modeling module is also used to construct a reliability redundancy allocation model for a multi-stage task system, as shown in formulas (7) and (8), based on the component constraints of different stages of the system and the total constraints of the system components, with the optimization goal of maximizing the reliability of the system task.

[0129] In one embodiment, the reliability redundancy allocation scheme determination module is further configured to use the initial feasible solution set of the genetic algorithm as the initial population; substitute the initial population into the genetic algorithm for further optimization, and update the population individuals through selection, crossover, and mutation to obtain the optimal solution of the reliability redundancy allocation scheme of the multi-stage task system with stage backup.

[0130] In one embodiment, the failed stage task will perform redundancy in the nearest backup stage; in the event of redundancy conflict, the task that comes earlier in the initial stage has higher priority; when performing redundancy in the backup stage, each redundant task is assigned only one component to the failed stage, and the remaining components are used to perform the initial task of the current stage.

[0131] Specific limitations regarding the reliability redundancy allocation device for multi-stage task systems with phased backups can be found in the limitations of the reliability redundancy allocation method for multi-stage task systems with phased backups mentioned above, and will not be repeated here. Each module in the aforementioned reliability redundancy allocation device for multi-stage task systems with phased backups 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 the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0132] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices 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 and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-stage task system reliability redundancy allocation method with staged backup. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0133] Those skilled in the art will understand that Figure 8The 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.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in the above method embodiment.

[0135] 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.

[0136] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. 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 patent application should be determined by the appended claims.

Claims

1. A method for reliability redundancy allocation in a multi-stage task system with staged backup, characterized in that, The method includes: Based on the component constraints at different stages of the system and the total constraints of the system components, a reliability redundancy allocation model for a multi-stage task system is constructed with the optimization objective of maximizing the reliability of the system task. Based on the working component information, stage backup scheme, and preset backup stage scheduling strategy of each stage in a multi-stage task system with stage backup, the approximate feasible path set and the number of feasible paths of the system are calculated using a path number approximation algorithm. Based on the approximate feasible path set and the reliability level of the feasible path of the system, the weighted value of the system path set under the preset allocation scheme is determined; Using the weighted values ​​of the system path set as heuristic factors, a set of initial feasible allocation scheme solutions is determined and used as the initial feasible solution set for the genetic algorithm; A genetic algorithm is used to solve the reliability redundancy allocation model to obtain a reliability redundancy allocation scheme for a multi-stage task system with stage backup.

2. The method according to claim 1, characterized in that, Based on the working component information, stage backup scheme, and preset backup stage scheduling strategy of each stage in a multi-stage task system with stage backup, an approximate feasible path set and the number of feasible paths of the system are calculated using a path number approximation algorithm, including: Based on the working component information, stage backup plan, and preset backup stage scheduling strategy for each stage, determine the initial feasible path set for each stage; The initial feasible path set of the initial stage is used as the current path set. The stage task set after adding the current stage task under each path is calculated. The path set of the current stage under each path is updated. After deleting the path set of the current stage under each path where the total number of failed tasks is greater than a preset value or the number of failed tasks of a certain task exceeds a preset value, the remaining path set of the current stage under each path is used as the current path set. The subsequent stages are processed until all stages are traversed, and the total path set of the system is obtained. Traverse the total path set of the system, delete paths for which the phase tasks have not yet been completed, and obtain the approximate feasible path set and the number of feasible paths of the system under a certain allocation scheme.

3. The method according to claim 1, characterized in that, Based on the approximate feasible path set and the reliability level of feasible paths in the system, the weighted value of the system path set under the preset allocation scheme is determined, including: The failure indication functions for each working component are constructed as follows: in, This is a failure indication function for the working component; The first in the system pathway i Each stage; To disregard component dependencies between stages Task failure rate; Based on the failure indication function of each working component in the system path within the approximate feasible path set of the system, the weighted path value based on the failure rate at each failure stage is obtained for each system path: in, For the first j The weighted path value of the system pathway based on the failure rate at each failure stage. For the first j One system pathway, For the first j The first system pathway i Failure indication function for each stage of the working component For the first j The first of the system pathways i Each stage h This represents the total number of working stages in the system pathway; Based on the weighted path value of each system path according to the failure rate at each failure stage, the weighted value of the system path set under the current allocation scheme is obtained as follows: in, Weight the system path set. This represents the total number of system pathways.

4. The method according to claim 1, characterized in that, Using the weighted values ​​of the system path set as heuristic factors, a set of initial feasible allocation solutions is determined, including: At the start of the allocation, there is no path in the system. Add a set of equipment to any subsystem of the multi-stage task system; calculate the change in the number of failed stage tasks in the system after adding a set of equipment; select the maximum value of the change in the number of failed stage tasks for allocation; if there are multiple stages with the same change in the number of failed stage tasks, allocate according to the principle of random allocation to obtain a set of initial feasible allocation solutions. Given that a system path already exists, calculate the change in the weighted value of the system path set after adding redundant devices to different subsystems; select the system path set with the largest change in weighted value for redundant device allocation; if multiple system path sets have the same change in weighted value, allocate according to the random allocation principle to obtain a set of initial feasible allocation solutions.

5. The method according to claim 1, characterized in that, Based on the constraints of components at different stages of the system and the overall constraints of the system components, with the optimization objective of maximizing the reliability of the system task, a reliability redundancy allocation model for a multi-stage task system is constructed as follows: in, For an allocation scheme, For the first The number of component redundancies in each subsystem The total number of sub-coefficients. To assess the reliability of system tasks under this allocation scheme, For the first The upper limit of the number of redundant components in a subsystem. This represents the upper limit of the system's constraints on component redundancy data.

6. The method according to claim 1, characterized in that, A genetic algorithm is used to solve the reliability redundancy allocation model to obtain a reliability redundancy allocation scheme for a multi-stage task system with staged backups, including: Use the initial set of feasible solutions as the initial population; The initial population is further optimized by substituting it into a genetic algorithm. Through selection, crossover, and mutation, the population individuals are updated to obtain the optimal solution for the reliability redundancy allocation scheme of a multi-stage task system with stage backup.

7. The method according to claim 1, characterized in that, The preset backup phase scheduling strategy includes: Failed phase tasks will be redundantly executed in the nearest backup phase; In the event of redundancy and conflict, the task that comes earlier in the initial stage has higher priority; When performing redundancy during the backup phase, each redundancy task is assigned only one component to the failure phase, while the remaining components perform the initial tasks of the current phase.

8. A reliability redundancy allocation device for a multi-stage task system with staged backup, characterized in that, The device includes: The reliability redundancy allocation model modeling module is used to construct a reliability redundancy allocation model for a multi-stage task system based on the component constraints at different stages of the system and the total constraints of the system components, with the optimization objective of maximizing the reliability of the system task. The system approximate feasible path set calculation module is used to calculate the approximate feasible path set and the number of feasible paths of the system based on the working component information of each stage in a multi-stage task system with stage backup, the stage backup scheme, and the preset backup stage scheduling strategy, using a path number approximation algorithm. The reliability redundancy allocation scheme determination module is used to determine the weighted value of the system path set under the preset allocation scheme based on the approximate feasible path set and the reliability level of the feasible path of the system; to determine the set of initial feasible allocation scheme solutions using the weighted value of the system path set as the heuristic factor, and to use it as the initial feasible solution set of the genetic algorithm; and to use the genetic algorithm to solve the reliability redundancy allocation model to obtain the reliability redundancy allocation scheme of the multi-stage task system with stage backup.

9. 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 method of any one of claims 1 to 7.

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