System reliability distribution method based on networked collaborative optimization, computer program product and terminal

Abstract

The invention discloses a system reliability distribution method based on networked collaborative optimization, a computer program product and a terminal, and belongs to the technical field of reliability distribution, and the method comprises the steps: building a network model of a system; determining a system reliability function; determining the reliability cost of each component unit of the system; determining constraint conditions of each component unit of the system; determining a multi-objective optimization model of system reliability distribution; and carrying out reliability distribution based on an improved networked collaborative multi-objective optimization algorithm. In the improved networked collaborative multi-objective optimization algorithm, the population evolution network is modeled, offspring with better genomes can be selected based on the out-degree of individuals to continue evolution, which is beneficial for determining that those individuals are excellent candidate solutions, and the diversity of populations can be kept to avoid premature convergence. And in combination with a forgetting mechanism, the global optimal solution can be solved, so that the reliability distribution effect is improved, and the total cost of the system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability allocation, and in particular to a system reliability allocation method based on networked collaborative optimization, a computer program product and a terminal. Background Art

[0002] When the reliability target of the system is determined, the process of reasonably allocating the quantitative system reliability index to each component unit is called reliability allocation. Reliability allocation is applicable to various types of systems, including systems involved in aircraft carrier projects and J-10 projects in the military industry, systems involved in aircraft, high-speed rail, high-end manufacturing in the civil field, multi-stage task systems, software systems, cluster systems, etc. Reliability allocation is an important part of reliability design, and the rationality of allocation will directly affect the reliability level of the system. In addition to the reliability target of the system, reliability allocation is often restricted by many other factors, such as the reliability improvement cost, complexity, technical level, working time, environmental conditions, importance, etc. of the product. In engineering practice, methods such as AGREE allocation method and scoring allocation method are often used for reliability allocation, which can comprehensively consider different constraints. However, reliability allocation is an iterative process, that is, a process of continuous "optimization". When the system to be allocated is more complex, its reliability model is often expressed as a network model, rather than a simple series, parallel, voting or bypass reliability model. The use of traditional reliability allocation methods will generate a huge workload, or even be impossible to implement, and there is no guarantee that a sufficiently optimal allocation scheme will be obtained. Therefore, optimization methods such as genetic algorithms have been tried for reliability allocation of complex systems, and in the process of autonomous optimization, relatively optimal reliability allocation solutions are obtained. However, reliability allocation in engineering practice is essentially a multi-objective optimization process. Existing reliability allocation methods based on optimization methods mostly use a weighted approach to merge multiple objectives into a cost function. This approach does not describe the solution in a detailed manner, and the design of the cost function is also a difficult problem. In addition, studies have shown that population structure has a significant impact on the performance of optimization algorithms, but the current related technologies for reliability allocation of complex systems based on multi-objective evolutionary algorithms have not taken this into account. At the same time, how to avoid or mitigate the tendency of evolutionary algorithms to fall into local optimality to obtain better allocation effects is also worthy of in-depth study. Summary of the invention

[0003] The purpose of the present invention is to overcome the problems of the prior art and to provide a system reliability allocation method based on networked collaborative optimization, a computer program product and a terminal.

[0004] The object of the present invention is to achieve the following technical solution: a system reliability allocation method based on networked collaborative optimization, the method comprising the following steps:

[0005] Establish the network model of the system according to the connection relationship between the system components;

[0006] The system reliability function is determined by using the minimum path set method or the minimum cut set method;

[0007] Determine the reliability cost of each component of the system;

[0008] Determine the constraints of each component of the system, including importance, complexity, technical level, working hours, and environmental conditions;

[0009] Determine a multi-objective optimization model for system reliability allocation, the optimization goal of the multi-objective optimization model is to minimize the difference between the system reliability allocation value and the reliability index, and the total cost generated by the system reliability allocation;

[0010] Reliability allocation based on networked collaborative multi-objective optimization algorithm includes the following sub-steps:

[0011] Define the encoding form, crossover operator, and mutation operator;

[0012] Population evolution network modeling: For individuals in a population, if the individual ind i and individual ind j Crossover generates new individuals ind l , then from ind i and ind j Connect directed edge to ind l ; If individual ind i Mutation produces new individuals l , then from ind i Connect directed edge to ind l , repeat the above directed edge connection operation of new individuals in each round of evolution, establish a population evolution network, and calculate the out-degree of all individuals in each generation of the population based on the population evolution network;

[0013] The fitness function is designed by minimizing the difference between the system reliability allocation value and the reliability index, the total cost generated by the system reliability allocation, and the out-degree of all individuals;

[0014] Design a forgetting mechanism: When the population similarity is greater than a given similarity threshold, individuals with quality lower than the quality threshold in the current population are selected for forgetting based on the Pareto optimal theory; at the same time, a random generation method is adopted to regenerate the population to maintain the population size and continue to participate in the next round of evolution under the condition of satisfying the constraints of the multi-objective optimization model;

[0015] The reliability allocation is carried out in combination with the multi-objective optimization algorithm. After the evolution is completed, the final population is output as the optimal solution to obtain the reliability allocation result of the system.

[0016] In one example, the network model of the system is G(V,E), where G represents the network model graph; V represents a set of component units, which serve as edges of the network model, and V = {v1, v2, ..., v n},v n represents the nth component unit, n is the number of component units; E represents the set of nodes of the network model, and E={e1,e2,…,e m},e m Represents the mth node, where m is the number of nodes.

[0017] In one example, the system reliability function is:

[0018]

[0019] in, Represents the reliability of the i-th component of the system, i,n represents the serial number label of the component; R S For system reliability.

[0020] In one example, the constraints of each component unit of the system are determined, including:

[0021] The importance of each component unit is determined according to the frequency of occurrence of each node. The higher the frequency, the higher the importance.

[0022] The expert scoring method is used to determine the complexity, technical level, working hours and environmental conditions of each component unit.

[0023] In one example, the multi-objective optimization model is:

[0024]

[0025]

[0026]

[0027] Among them, V-min means that the optimization goal is to minimize; f R (x) is the difference between the system reliability distribution value and the reliability index; f cost (x) is the total cost of system reliability allocation; st represents the allocation constraint; v i represents the i-th component unit; v n Represents the nth component unit; Indicates importance; Indicates the level of complexity; Indicates the level of technology; Indicates working hours; Indicates environmental conditions; Respectively represent the constituent units vi The upper and lower limits of reliability; R s assign Represents the system reliability distribution value; R s target Indicates the system reliability index requirements; Represents the cost of the i-th component unit.

[0028] In one example, the encoding form is defined, including:

[0029] Real number coding is used, each real number represents the reliability allocation value of a component unit, and the reliability allocation value of a single component unit is the individual ind i A gene of

[0030] Define the crossover operator, including:

[0031] Select two individuals ind i and ind j , define the crossover probability as r c ; Randomly select ind i n×r c Gene replacement ind j The genes at the corresponding positions in generate new individuals ind k ; If the crossover operation causes the gene to fail to meet the constraints of the multi-objective optimization model, terminate the crossover;

[0032] Define mutation operators, including:

[0033] For any individual ind i , define the mutation probability as r m , randomly select n×r m The genes are randomly mutated. If the mutation operation causes the gene to fail to meet the constraints of the multi-objective optimization model, the mutation is terminated.

[0034] In one example, the fitness function f fitness The expression is:

[0035] f fitness =min[f R (x),f cost (x),f outdeg (x)]

[0036]

[0037] Among them, f R (x) is the difference between the system reliability distribution value and the reliability index; f cost (x) the total cost incurred for system reliability allocation; represents the out-degree of an individual, which is the sum of the number of outgoing edges of the individual; foutdeg (x) represents the inverse of the out-degree of an individual.

[0038] In one example, the population similarity IndSim pop The calculation expression is:

[0039]

[0040] Among them, IndSim i,j represents the similarity between individuals in each generation; k represents the population size; ind i represents the i-th individual in the population; ind j represents the jth individual in the population; Represents the matching coefficient.

[0041] It should be further explained that the technical features corresponding to the above examples can be combined or replaced with each other to form a new technical solution.

[0042] The present invention also includes a computer program product, including a computer program, which, when executed by a processor, implements the steps of the system reliability allocation method based on networked collaborative optimization formed by any one of the above examples or a combination of multiple examples.

[0043] The present invention also includes a terminal, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and when the processor executes the computer instructions, the steps of the system reliability allocation method based on networked collaborative optimization formed by any one or more of the above examples are executed.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The method of the present invention comprehensively considers various constraints such as cost, technical level, environmental conditions, etc., and uses an improved networked collaborative multi-objective optimization algorithm to automatically allocate system reliability. It can quickly provide a variety of potential allocation schemes that meet the system reliability requirements for decision-making, which can significantly improve the reliability allocation efficiency and reliability allocation effect of complex systems, and minimize system costs. At the same time, in the improved networked collaborative multi-objective optimization algorithm, the population evolution network is modeled, and the offspring with better genomes can be selected based on the out-degree of the individuals to continue evolving, which is conducive to determining which individuals are excellent candidate solutions, and can maintain the diversity of the population to avoid premature convergence. Combined with the forgetting mechanism, the global optimal solution can be solved, thereby improving the reliability allocation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The specific implementation methods of the present invention are further described in detail below in conjunction with the accompanying drawings. The accompanying drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to represent the same or similar parts. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0047] Figure 1 A method flow chart provided for an example of the present invention;

[0048] Figure 2 A network model diagram of an airborne equipment system provided as an example of the present invention;

[0049] Figure 3 A networked collaborative optimization flow chart based on NSGA-II provided as an example of the present invention;

[0050] Figure 4 A schematic diagram of the evolution process of two rounds of evolution of a networked collaborative optimization algorithm provided as an example of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In the description of the present invention, it should be noted that the directions or positional relationships indicated by "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are directions or positional relationships based on the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the use of ordinal numbers (e.g., "first and second", "first to fourth", etc.) is to distinguish objects, and is not limited to this order, and cannot be understood as indicating or implying relative importance.

[0053] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] In one example, if Figure 1 As shown, a system reliability allocation method based on networked collaborative optimization includes the following steps:

[0056] S10: Establish a network model of the system according to the connection relationship between the system components.

[0057] Among them, any complex product system with reliability allocation requirements, such as high-speed rail, ships, etc., can use the method of the present invention for reliability allocation. The network model of the system is the network diagram of the system, which can be a directed acyclic graph. The system component unit can be a component of the system, which can be a subsystem or component module or component unit of the system.

[0058] S20: Determine the system reliability function using the minimum path set method or the minimum cut set method.

[0059] The system reliability function is used to describe the probability that the system will successfully perform its functions under given conditions. This function is usually expressed in terms of the reliability of the system's components.

[0060] S30: Determine the reliability cost of each component unit of the system, and determine the constraints of each component unit of the system, including relative importance, complexity, technical level, working hours, and environmental conditions.

[0061] Among them, determining the reliability cost of each component unit of the system and determining the constraint conditions of each component unit of the system can be carried out simultaneously or one after another. In this example, based on engineering experience, the reliability cost of each component unit of the system is determined. i Every 1×10 increase in reliability -2 The corresponding cost is denoted as cost vi .

[0062] S40: Determine a multi-objective optimization model for system reliability allocation. The optimization goal of the multi-objective optimization model is to minimize the difference between the system reliability allocation value and the reliability index, and the total cost generated by the system reliability allocation. The constraints include relative importance, complexity, technical level, working hours, and environmental conditions.

[0063] S50: Reliability allocation based on a networked collaborative multi-objective optimization algorithm, including the following sub-steps:

[0064] S501: Define the encoding format, crossover operator, and mutation operator.

[0065] Among them, encoding is the process of abstracting the feasible solution of the optimization problem into a variable sequence. This example uses real number encoding. The crossover operator combines some genes of two parent individuals to produce offspring, simulating the evolutionary process; the mutation operator can simulate gene mutation and increase the diversity of the population by randomly changing some genes of individuals.

[0066] S502: Population evolution network modeling: For individuals in a population, if the individual ind i and individual ind j Crossover generates new individuals ind l , then from ind i and ind j Connect directed edge to ind l ; If individual ind i Mutation produces new individuals l , then from ind i Connect directed edge to ind l In each round of evolution, the above directed edge connection operation of new individuals is repeated to establish a population evolution network, and the out-degree of all individuals in each generation of the population is calculated based on the population evolution network. indi , which is the sum of the number of outgoing edges of the individuals.

[0067] S503: Design a fitness function by minimizing the difference between the system reliability allocation value and the reliability index, the total cost generated by the system reliability allocation, and the out-degree of all individuals.

[0068] S504: Design a forgetting mechanism: When the population similarity is greater than the given similarity threshold, it is considered that the population diversity is insufficient, the algorithm has fallen into a local optimum, and continued evolution no longer makes a beneficial contribution. At this time, based on the Pareto optimal theory, individuals with quality below the quality threshold in the current population are selected for forgetting. The quality threshold in this example is 50%, that is, based on the Pareto optimal theory, half of the individuals with the relatively worst quality in the current population are selected and deleted from the current population for forgetting; at the same time, a random generation method is used to regenerate half of the individuals in the population size under the condition of satisfying the constraints of the multi-objective optimization model, and add them to the current population to maintain the population size k and continue to participate in the next round of evolution.

[0069] S505: Reliability allocation is performed in combination with a multi-objective optimization algorithm. After the evolution is completed, the final population is output as the optimal solution to obtain the reliability allocation result of the system. Among them, the multi-objective optimization algorithm includes but is not limited to NSGA-II, SPEA2, PAES, etc. The above multi-objective optimization algorithm is used as the basic algorithm to design a corresponding networked collaborative optimization algorithm for reliability allocation.

[0070] The present invention proposes a complex system reliability allocation method based on networked collaborative optimization. Specifically, for a complex system, it is first modeled as a network model, and the calculation expression of the system reliability is determined by using the minimum path set method or the minimum cut set method, and the importance of each component of the system is determined by using complex network theory, and then the complexity, technical level, working time, environmental conditions and other constraints of each component product are determined, and finally the improved multi-objective evolutionary algorithm is used to allocate the system reliability, so that the system reliability meets the allocation requirements while satisfying various constraints, and the cost is minimized.

[0071] In one example, establishing a network model includes:

[0072] According to the system components, the interconnection relationship between the components of the system is clarified, and the network model of the system is established. According to the system reliability block diagram, each box representing the component unit is represented by an arc, and then the nodes are marked at the connection of each box to form the network diagram (network model) of the system. The present invention represents the network diagram G as G(V,E), and V={v1,v2,…,v n}, represents the set of system components, that is, the edge in the network graph, n is the number of units; E = {e1, e2, …, e m}, represents the set of network graph nodes, and m is the number of nodes.

[0073] In one example, based on the established network model, the minimum path set method or the minimum cut set method is used to determine the system reliability function as follows:

[0074]

[0075] in, represents the reliability of the i-th component of the system, i,n represents the reliability label of the component; R S For system reliability.

[0076] In one example, the constraints of each component unit of the system are determined, including:

[0077] The importance is determined based on complex network theory, and other constraints are determined by expert scoring. Specifically, the motif is the basic microstructure in the network system. All three-node motifs of the network are mined, and the frequency of occurrence of each node in all motifs is calculated. Node The higher the value, the more important the node is, which is the basic component of the whole system. For other constraints, an expert scoring method is used to determine the relative complexity of each component unit. Technical Level Working hours Environmental conditions The scoring range of each constraint is 1 to 10. The higher the score, the worse the impact of the constraint on product reliability. Therefore, the complexity of the product is 1 point for the simplest and 10 points for the most complex; the highest technical level is 1 point and the lowest is 10 points; the shortest working time is 1 point and the longest is 10 points; the best environmental conditions are 1 point and the worst are 10 points.

[0078] In one example, the system reliability allocation is essentially a multi-objective optimization model, which is expressed as follows:

[0079]

[0080] Among them, V-min means that the optimization goal is to minimize; f R (x) is the difference between the system reliability distribution value and the reliability index; f cost (x) is the total cost of system reliability allocation; st represents the allocation constraint; v i represents the i-th component unit; v n Indicates the nth component unit; Indicates importance; Indicates the level of complexity; Indicates the technical level, that is, the more important the system component is, the higher the reliability is assigned; the higher the complexity is, the lower the reliability is assigned; the lower the technical level is, the lower the reliability is assigned; the longer the working time is, the lower the reliability is assigned; the worse the working environment is, the lower the reliability is assigned; Indicates working hours; Indicates environmental conditions; Respectively represent the constituent units v i The upper and lower limits of reliability can be determined based on experience; R s assign Represents the system reliability distribution value; R s target Indicates the system reliability index requirements; Represents the cost of the i-th component unit.

[0081] In one example, defining the encoding form, crossover operator, and mutation operator of the networked collaborative optimization multi-objective optimization algorithm proposed in the present invention includes the following steps:

[0082] Define the encoding form, including: using real number encoding, for the n components of the system, each real number Represents a unit v i The reliability distribution value of individual ind i A gene, so there are individuals Assuming that the population size is k, then pop = [ind1,…,indi ,…,ind k ]. Each individual in the population represents a reliability allocation result, and the population represents k reliability allocation results.

[0083] Define the crossover operator, including: select two individuals ind i and ind j , assuming the crossover probability is r c , randomly select ind i n×r c Gene, replace ind j The genes at the corresponding positions in generate new individuals ind k If the crossover operation causes the gene value to fail to meet the constraints of the multi-objective optimization model, the crossover is terminated.

[0084] Define mutation operators, including: for any individual ind i , assuming the mutation probability is r m , randomly select n×r m Genes are randomly mutated, that is, the selected n×r m The value of each gene is randomly increased or decreased by a random number R rc , R rc The value of can be determined based on experience. If the mutation operation causes the gene to fail to meet the constraints of the multi-objective optimization model, the mutation is terminated.

[0085] In one example, the fitness function f fitness The expression is:

[0086] f fitness =min[f R (x),f cost (x),f outdeg (x)]

[0087]

[0088] Among them, f R (x) is the difference between the system reliability distribution value and the reliability index; f cost (x) the total cost incurred for system reliability allocation; represents the out-degree of an individual, which is the sum of the number of outgoing edges of the individual; f outdeg (x) represents the inverse of the out-degree of an individual. In this example, f fitness As the fitness function, the Pareto optimality is used to compare the advantages and disadvantages of different individuals in order to retain high-quality individuals.

[0089] The fitness function designed in this example not only takes into account the reliability distribution and cost of the system, but also uses the out-degree of individuals in the population evolution network Taking the structure of the population into consideration, the larger the out-degree of an individual, the more likely it is to produce excellent offspring through evolution and have a better genome. Therefore, it should be selected for subsequent evolution to achieve collaborative optimization based on the network structure.

[0090] For population initialization, when the first generation of population is randomly generated, there is no crossover mutation operation, and the population evolution network modeling is not performed at this time. The f of all individuals outdeg (x) are all forced to 1, and the subsequent population is modeled by population evolution network and the individual f is calculated outdeg (x).

[0091] In one example, calculating population similarity in the forgetting mechanism includes:

[0092] During the evolution process, cosine similarity is used to calculate the similarity between individuals in each generation of the population:

[0093]

[0094] Among them, IndSim i,j Indicates the similarity between individuals in each generation; ind i represents the i-th individual in the population; ind j represents the jth individual in the population.

[0095] Then calculate the average similarity IndSim of the population pop :

[0096]

[0097] Where k represents the population size; Represents the matching coefficient.

[0098] The above-mentioned multiple implementation step examples of reliability allocation based on networked collaborative multi-objective optimization algorithm are combined, and on the basis of clarifying key operations such as encoding method, crossover operator, mutation operator, fitness function and forgetting mechanism, the existing multi-objective optimization algorithm is integrated to perform reliability allocation. After the evolution is completed, the final population is output as the optimal solution, which is the final generated reliability allocation result.

[0099] The above examples are combined to illustrate the method of the present invention by taking a certain airborne equipment system as an example. At this time, the airborne equipment system includes 5 subsystems, namely, engine, receiver, take-off automatic device, control equipment and power supply. The system reliability index is 0.95, and the corresponding reliability needs to be allocated to each subsystem level. The specific allocation steps are as follows:

[0100] S100: Determine the reliability model and reliability function of the system according to the five subsystems of the airborne equipment system;

[0101] After analyzing the composition of the airborne equipment system, its reliability model cannot be expressed as a classic reliability model such as series, parallel, voting, etc., but a network model, such as Figure 2 As shown. Based on the network diagram, the minimum path set method is used to analyze the network model, and the minimum path set of the network model is S = {v1v3v5, v2v3v4, v1v4, v2v5}. Therefore, its reliability function R S 5 for:

[0102]

[0103] S200: Determine a system reliability allocation model;

[0104] First, determine the reliability improvement cost of each subsystem Then determine the reliability improvement cost function

[0105] Secondly, determine the relative complexity of the constraints based on engineering experience and expert scoring Technical Level Working hours Environmental conditions In addition, complex network motif theory is used to determine the importance of each subsystem.

[0106]

[0107] In the reliability function Reliability Improvement Cost Function Based on the reliability constraints of the subsystem, the reliability allocation model of the product is obtained as follows:

[0108]

[0109] S300: Use networked collaborative multi-objective optimization algorithm to allocate system reliability;

[0110] According to the key operations such as clear encoding, crossover, mutation, population evolution network modeling, fitness function and forgetting mechanism, and then setting the maximum number of evaluations, population size, crossover probability, mutation probability, population similarity threshold and other parameters, reliability allocation can be carried out based on the existing multi-objective evolutionary algorithm. Figure 3 The networked collaborative optimization algorithm flow based on NSGA-II is given, which integrates the key operations such as population evolution network modeling, fitness function and forgetting mechanism proposed in the present invention, including the following steps:

[0111] S3001: Population initialization;

[0112] S3002: Population evolution network modeling;

[0113] S3003: Calculate fitness function;

[0114] S3004: Determine whether the termination condition is met. If so, output the final population, the evolution ends, and the reliability distribution result of the system is obtained; if not, proceed to step S3005;

[0115] S3005: Determine whether the population diversity is insufficient, if not, proceed to step S3008, if yes, proceed to step S3006;

[0116] S3006: Trigger the forgetting mechanism to forget the corresponding individual;

[0117] S3007: Fast non-dominated sorting and calculation of crowding distance to compare the quality of individuals;

[0118] S3008: Generate a new population through selection, crossover and mutation operations, and return to step S3002.

[0119] In this implementation, the algorithm parameters are set as shown in Table 1. In this embodiment, multiple rounds of evolution can usually be performed to obtain the best reliability allocation effect.

[0120] Table 1 Parameter settings

[0121]

[0122]

[0123] Hypervolume is a common indicator for measuring the performance of multi-objective optimization algorithms. In this embodiment, the hypervolume is used to evaluate the evolution process of the two-round evolution of the networked collaborative optimization algorithm proposed in the present invention. Figure 4 As shown in Figure 2, from the perspective of the hypervolume index, the algorithm of the present invention can achieve a better allocation effect. After a certain number of evolutionary generations, the hypervolume is close to 1.

[0124] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the system reliability allocation method based on networked collaborative optimization formed by any one of the above examples or a combination of multiple examples. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0125] The present invention also provides a terminal, which has the same inventive concept as any example or a combination of multiple examples corresponding to the above-mentioned system reliability allocation method based on networked collaborative optimization, including a memory and a processor, wherein the memory stores computer instructions that can be run on the processor, and the processor executes the steps of the above-mentioned system reliability allocation method based on networked collaborative optimization when running the computer instructions. The processor can be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0126] In one example, the terminal, i.e., the electronic device, is presented in the form of a general-purpose computing device, and the components of the electronic device may include but are not limited to: at least one processing unit (processor) mentioned above, at least one storage unit mentioned above, and a bus connecting different system components (including storage units and processing units).

[0127] The storage unit stores a program code, and the program code can be executed by the processing unit, so that the processing unit performs the steps of various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification. For example, the processing unit can execute the above-mentioned system reliability allocation method based on networked collaborative optimization.

[0128] The storage unit may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit, and may further include a read-only memory unit (ROM).

[0129] The storage unit may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0130] The bus may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0131] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0132] Through the above description, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to this exemplary embodiment can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method of the exemplary embodiment of the present application.

[0133] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A system reliability allocation method based on networked collaborative optimization, characterized in that: The following steps are involved: Establish the network model of the system according to the connection relationship between the system components; The system reliability function is determined by using the minimum path set method or the minimum cut set method; Determine the reliability cost of each component of the system; Determine the constraints of each component of the system, including importance, complexity, technical level, working hours, and environmental conditions; Determine a multi-objective optimization model for system reliability allocation, the optimization goal of the multi-objective optimization model is to minimize the difference between the system reliability allocation value and the reliability index, and the total cost generated by the system reliability allocation; Reliability allocation based on networked collaborative multi-objective optimization algorithm includes the following sub-steps: Define the encoding form, crossover operator, and mutation operator; Population evolution network modeling: For individuals in a population, if the individual ind i and individual ind j Crossover generates new individuals ind l , then from ind i and ind j Connect directed edge to ind l ; If individual ind i Mutation produces new individuals l , then from ind i Connect directed edge to ind l , repeat the above directed edge connection operation of new individuals in each round of evolution, establish a population evolution network, and calculate the out-degree of all individuals in each generation of the population based on the population evolution network; The fitness function is designed by minimizing the difference between the system reliability allocation value and the reliability index, the total cost generated by the system reliability allocation, and the out-degree of all individuals; Design a forgetting mechanism: When the population similarity is greater than a given similarity threshold, individuals with quality lower than the quality threshold in the current population are selected for forgetting based on the Pareto optimal theory; at the same time, a random generation method is adopted to regenerate the population to maintain the population size and continue to participate in the next round of evolution under the condition of satisfying the constraints of the multi-objective optimization model; The reliability allocation is carried out in combination with the multi-objective optimization algorithm. After the evolution is completed, the final population is output as the optimal solution to obtain the reliability allocation result of the system.

2. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: The network model of the system is G(V,E), where G represents the network model graph; V represents the set of component units, which serve as the edges of the network model, and V = {v1,v2,…,v n },v n represents the nth component unit, n is the serial number label of the component unit; E represents the set of nodes of the network model, and E={e1,e2,…,e m },e m Represents the mth node, where m is the number of nodes.

3. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: The system reliability function is: in, Indicates the system component v i The reliability of i, n represents the serial number label of the component unit; R S For system reliability.

4. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: Determine the constraints of each component of the system, including: The importance of each component unit is determined according to the frequency of occurrence of each node. The higher the frequency, the higher the importance. The expert scoring method is used to determine the complexity, technical level, working hours and environmental conditions of each component unit.

5. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: The multi-objective optimization model is: Among them, V-min means that the optimization goal is to minimize; f R (x) is the difference between the system reliability distribution value and the reliability index; f cost (x) is the total cost of system reliability allocation; st represents the allocation constraint; v i represents the i-th component unit; v n Represents the nth component unit; Indicates importance; Indicates the level of complexity; Indicates the level of technology; Indicates working hours; Indicates environmental conditions; Respectively represent the constituent units v i The upper and lower limits of reliability; Represents the system reliability distribution value; R s target Indicates the system reliability index requirements; Represents the cost of the i-th component unit.

6. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: Define the encoding format, including: Real number coding is used, each real number represents the reliability allocation value of a component unit, and the reliability allocation value of a single component unit is the individual ind i A gene of Define the crossover operator, including: Select two individuals ind i and ind j , define the crossover probability as r c ; Randomly select ind i n×r c Gene replacement ind j The genes at the corresponding positions in generate new individuals ind k ; If the crossover operation causes the gene to fail to meet the constraints of the multi-objective optimization model, terminate the crossover; Define mutation operators, including: For any individual ind i , define the mutation probability as r m , randomly select n×r m The genes are randomly mutated. If the mutation operation causes the gene to fail to meet the constraints of the multi-objective optimization model, the mutation is terminated.

7. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: The fitness function f fitness The expression is: f fitness =min[f R (x),f cost (x),f outdeg (x)] Among them, f R (x) is the difference between the system reliability distribution value and the reliability index; f cost (x) the total cost incurred for system reliability allocation; represents the out-degree of an individual, which is the sum of the number of outgoing edges of the individual; f outdeg (x) represents the inverse of the out-degree of an individual.

8. The system reliability allocation method based on networked collaborative optimization according to claim 1 is characterized in that: The population similarity IndSim pop The calculation expression is: Among them, IndSim i,j represents the similarity between individuals in each generation; k represents the population size; ind i represents the i-th individual in the population; ind j represents the jth individual in the population; Represents the matching coefficient.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the system reliability allocation method based on networked collaborative optimization described in any one of claims 1 to 8 are implemented.

10. A terminal comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, characterized in that: When the processor runs the computer instructions, the processor performs the steps of the system reliability allocation method based on networked collaborative optimization according to any one of claims 1 to 8.

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