Ship electronic information system fault prevention and control method based on backup reconstruction

Abstract

The invention discloses a ship electronic information system fault prevention and control method based on backup reconstruction, and belongs to the technical field of ship electronic information system fault prevention and control. The method aims at solving the problem that dynamic balance between backup cost and reliability guarantee is difficult to achieve due to the fact that redundant resource configuration excessively depends on manual experience presetting. Comprising the following steps: establishing a target function by taking minimization of the total reinforcement cost of resource hardware as a target; the target function is solved through a rime optimization algorithm based on the dynamic inertia weight, decision variables corresponding to all resource hardware serve as rime bodies to form a rime population, all possible solutions of each rime body serve as rime particles, and an expression of the rime population based on the rime particles is obtained; a soft rime search mechanism and a hard rime puncture mechanism are combined for searching to obtain an optimal candidate solution; and an optimal solution is determined through a forward greedy selection mechanism. The method is used for electronic information system fault prevention and control.

Description

Technical Field

[0001] The present invention relates to a method for preventing and controlling a fault of a ship electronic information system based on backup reconstruction, and belongs to the technical field of preventing and controlling a fault of a ship electronic information system. Background Art

[0002] As a key component of modern ship combat systems, the mission reliability of ship electronic information systems is crucial to ensuring combat effectiveness. The widespread adoption of a platform-based architecture based on a public computing environment in the new generation of ship electronic information systems has resulted in unprecedented levels of integration and hardware-software coupling. This makes fault propagation paths more complex and insidious. In the harsh maritime combat environment, mission reliability assessment and assurance face severe challenges, directly threatening the core combat capability and continued operational resilience of ships.

[0003] Traditional single-machine, single-use shipboard electronic information systems rely on fault prevention strategies based on hardware redundancy and threshold control, resulting in inherent flaws such as rigid resource configuration and limited functional scalability. In contrast, new architectures based on a public computing environment leverage virtualization technology within the General System Management (GSM) framework to achieve global resource scheduling. This architecture, combined with a dynamic reconstruction mechanism featuring dynamic function chain reconstruction and resource remapping, provides dynamic fault propagation prevention and control capabilities through a four-dimensional governance system consisting of health monitoring (HM), fault management (FM), configuration management (CM), and security management (SM). However, research has shown that existing prevention and control strategies still have limitations when addressing dynamic resource failure scenarios. Specifically, first, redundant resource configuration relies too much on manual experience-based pre-setting, which makes it difficult to achieve a dynamic balance between backup costs and reliability assurance; second, the fault response process is limited by the rule-driven decision-making mechanism, which makes it impossible to coordinate and optimize multi-dimensional constraints such as migration delay, system reliability and load balancing; finally, the static priority strategy adopted for security function assurance is prone to cause double negative effects, as the execution of core functional chain tasks is hindered due to insufficient resource release, and the dependency chain is broken due to the disorderly abandonment of non-critical application software. Summary of the Invention

[0004] Aiming at the problem that redundant resource configuration relies too much on manual experience preset, which makes it difficult to achieve a dynamic balance between backup cost and reliability assurance, the present invention provides a ship electronic information system fault prevention and control method based on backup reconstruction.

[0005] The present invention provides a method for preventing and controlling a fault of a ship electronic information system based on backup and reconstruction, comprising: For the resource hardware of the ship's electronic information system, the system reliability is expressed in the form of a weighted product of decision variables and single-node static reliability. Based on the establishment of system reliability and decision variable constraints, an objective function is established with the goal of minimizing the total reinforcement cost of resource hardware. The objective function is solved by using a rime optimization algorithm based on dynamic inertia weight. The decision variables corresponding to all resource hardware are regarded as rime bodies to form rime populations. All possible solutions of each rime body are regarded as rime particles. The expression of rime population based on rime particles is obtained. A soft rime search mechanism is used to perform a global search to update the rime population; a hard rime puncture mechanism is then used to perform a local fine search to determine the optimal rime particles corresponding to each rime body as the optimal candidate solution; the optimal candidate solutions obtained in adjacent iterations are then evaluated through a forward greedy selection mechanism to determine the current optimal solution for the rime body; the soft rime search mechanism is then returned to repeat the iterative update process until the final optimal solution for the decision variables is obtained; and hot backup of resource hardware is performed based on the final optimal solution for the decision variables.

[0006] According to the ship electronic information system fault prevention and control method based on backup reconstruction of the present invention, it is assumed that the ship electronic information system includes Resource hardware, resource hardware i As a node i Single-node static reliability , ; for nodes i Decision variables for reinforcement ; The static reliability of a single node after the node operation is expressed as ; When the node i Reinforcement, for: ; When the node i Without reinforcement, the static reliability of a single node remains ;Right now ; by Indicates the node i The cost of reinforcement; Representation node The weighting given to system reliability; The system reliability is expressed as : ; Further expanded to: .

[0007] According to the ship electronic information system fault prevention and control method based on backup reconstruction of the present invention, the objective function is: ; The constraints are: , .

[0008] According to the ship electronic information system fault prevention and control method based on backup reconstruction of the present invention, the rime population is represented as , rime population include Rime body: , In the formula Rime No. j rime particles; Then we get the expression of rime population based on rime particles: .

[0009] According to the ship electronic information system fault prevention and control method based on backup reconstruction of the present invention, the method of using the soft rime search mechanism to perform global search and update the rime population is as follows: Use the rime particle position vector to describe the search state: , In the formula For the i The first j The updated position of the rime particles, It is the best rime body in the current rime population. j The optimal position of rime particles, is a random parameter in the interval (-1,1); is the dynamic adjustment coefficient, is the environmental adjustment coefficient, is a random variable between (0,1), for The moving upper boundary of for The moving lower boundary of is a random number in the range (0,1). is the inter-particle adhesion effect parameter: , In the formula is the current iteration number, is the maximum number of iterations; Indicates rounding; is the dynamic inertia weight.

[0010] According to the ship electronic information system fault prevention and control method based on backup reconstruction of the present invention, the dynamic inertia weight for: , In the formula is the initial inertia weight, is the final inertia weight.

[0011] According to the backup and reconstruction-based fault prevention and control method for ship electronic information systems of the present invention, a method for using a hard rime puncture mechanism to perform a local fine search to determine the optimal rime particles corresponding to each rime body includes: , In the formula is a random number between (-1,1), It is the normalized value of the fitness value of the current candidate solution.

[0012] According to the fault prevention and control method of the ship electronic information system based on backup reconstruction of the present invention, the optimal candidate solution obtained in adjacent iterations is evaluated by a forward greedy selection mechanism, and the method for determining the current optimal solution of the rime body is as follows: the forward greedy selection mechanism compares the fitness of the optimal candidate solution obtained in adjacent iterations, and when the new optimal candidate solution is better than the historical optimal candidate solution, dynamic replacement is triggered to obtain the current optimal solution.

[0013] Beneficial effects of the present invention: The present invention is suitable for solving the problems of reliability assurance and dynamic fault prevention and control of ship electronic information systems in complex combat environments.

[0014] The present invention proposes the idea of ​​backup reconstruction, which is particularly suitable for the early stage of fault propagation. The proposed backup reconstruction mechanism is oriented towards the early stage of fault prevention and control triggered by resource failure. It realizes rapid fault response and system function maintenance by pre-setting backup modules. Its core operating logic is reflected in that when the main module fails, the backup module can be immediately switched to a working state, thereby effectively blocking the fault conduction path and reducing the risk of system cascade collapse caused by resource failure. Based on this research framework, the method of the present invention first establishes a backup resource allocation model with redundancy cost minimization as the core, and converts parameters such as resource reliability and function importance into system-level constraints. In response to the low optimization efficiency and local optimal trap problems of traditional optimization methods, an improved rime algorithm is further proposed. By introducing a linear adaptive inertia weight adjustment mechanism, a dynamic balance between global exploration capability and local development capability is achieved, thereby improving the accuracy of model solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the method for preventing and controlling failures of a ship electronic information system based on backup and reconstruction according to the present invention; Figure 2 It is a schematic diagram of the update process of the ship electronic information system fault prevention and control method based on backup reconstruction described in the present invention.

[0016] Figure 3 It is a schematic diagram of the reliability distribution of resource hardware; Figure 4 This is a schematic diagram of the reinforcement cost of resource hardware; Figure 5 This is a diagram of the reliability and cost curves of the original frost and ice algorithm optimization process in experimental verification; Figure 6 This is a schematic diagram of the reliability and cost curve of the method of the present invention in experimental verification; Figure 7 This is a diagram of the reliability and cost curve of the whale optimization algorithm in experimental verification; Figure 8 This is a diagram of the reliability and cost curves of the Northern Goshawk algorithm in experimental verification. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0018] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0019] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0020] Specific implementation method 1. Combination Figure 1 and Figure 2 As shown, the present invention provides a method for preventing and controlling faults in a ship electronic information system based on backup reconstruction, comprising: For the resource hardware of the ship's electronic information system, the system reliability is expressed in the form of a weighted product of decision variables and single-node static reliability. Based on the establishment of system reliability and decision variable constraints, an objective function is established with the goal of minimizing the total reinforcement cost of resource hardware. The objective function is solved by using a rime optimization algorithm based on dynamic inertia weight. The decision variables corresponding to all resource hardware are regarded as rime bodies to form rime populations. All possible solutions of each rime body are regarded as rime particles. The expression of rime population based on rime particles is obtained. A soft rime search mechanism is used to perform a global search to update the rime population; a hard rime puncture mechanism is then used to perform a local fine search to determine the optimal rime particles corresponding to each rime body as the optimal candidate solution; the optimal candidate solutions obtained in adjacent iterations are then evaluated through a forward greedy selection mechanism to determine the current optimal solution for the rime body; the soft rime search mechanism is then returned to repeat the iterative update process until the final optimal solution for the decision variables is obtained; and hot backup of resource hardware is performed based on the final optimal solution for the decision variables.

[0021] This embodiment constructs a reliability-cost dual-objective optimization model and adopts an improved rime algorithm to dynamically adjust the backup node selection strategy to achieve intelligent configuration of redundant resources, which can suppress the spread of faults at the source.

[0022] This embodiment includes three main processing steps: 1) Modeling of backup and reconstruction mechanisms; 2) Rime optimization algorithm based on dynamic inertia weight; 3) Experimental verification.

[0023] The following is a detailed description: 1) Modeling of backup and reconstruction mechanisms; Assume that the ship's electronic information system includes Resource hardware, resource hardware i As a node i Single-node static reliability , ; for nodes i Decision variables for reinforcement ;in Indicates the node Strengthen the system by using hot backup and adding an identical resource hardware. Indicates no reinforcement.

[0024] The static reliability of a single node after the node operation is expressed as ; When the node i Reinforcement, for: ; When the node i Without reinforcement, the static reliability of a single node remains ;Right now ; by Indicates the node i The cost of reinforcement; Representation node The weighting given to system reliability; Considering the different contributions of each resource hardware to the system reliability, the system reliability is expressed as the weighted product of the reliability of each node: : ; Due to the reliability of each node Reinforced decision variables The influence, combined with the expression of reliability after node reinforcement, is further expanded into: .

[0025] On this basis, the goal is to minimize the total reinforcement cost as the objective function, which is expressed as: ; To ensure that the system reliability meets the requirements, the following constraints must be met: , At the same time, the decision variables The following binary constraints must be met: .

[0026] 2) Rime optimization algorithm based on dynamic inertia weight; The Rime Optimization Algorithm (RIME), developed by Su et al. in 2023, is a new intelligent optimization method inspired by natural phenomena. The algorithm simulates the formation mechanism of two forms of rime in nature - soft rime and hard rime, and innovatively designs a soft rime search mechanism and a hard rime puncture mechanism, which correspond to the global exploration and local development stages of the optimization process, respectively. To improve search efficiency, the algorithm also introduces a preferential mechanism based on a greedy strategy, which achieves continuous optimization of the solution by dynamically updating the population. The entire algorithm framework consists of four core modules: initial population generation, wide-area search based on soft rime, local fine search of hard rime structure, and preferential update strategy.

[0027] (1) Population initialization The rime population is represented as , rime population include Rime body: , In the formula Rime No. j rime particles; Then we get the expression of rime population based on rime particles: .

[0028] (2) Soft rime search mechanism The RIME algorithm innovatively models the dynamics of soft rime, constructing a multidimensional search architecture by extracting five of its dynamic characteristics. This modeling approach demonstrates strong global exploration capabilities and the ability to escape local extremes during the optimization process. Mathematically, the algorithm uses particle position vectors to describe the search state, which can be expressed as the following formula.

[0029] ; , In the existing methods, Used to control the number of steps in the step function.

[0030] This implementation incorporates Eberhart's time-varying inertia coefficient theory into the RIME algorithm framework, constructing an optimization enhancement mechanism based on dynamic parameter adjustment. By establishing a time-varying inertia coefficient model, the algorithm achieves both improved environmental adaptability and improved parameter self-regulation capabilities. At the technical implementation level, a linear time-varying adjustment strategy is employed, enabling the algorithm to autonomously adjust its search pattern based on the iterative process.

[0031] In the original soft rime search update formula, Indicates the position of the best candidate solution in the current population and provides global excellent information; and Together they form a direction factor, which is used to randomly adjust the search direction; Usually a regulatory factor related to the environment or the number of iterations, affecting the search step length; Indicates the degree of adhesion between frost particles, which controls the update of candidate solutions and depends on the upper bound of the search space and the lower bound range.

[0032] Combine Figure 1 and Figure 2 As shown in the figure, the method of using the soft rime search mechanism to perform global search and update the rime population is: Introducing dynamic inertia weight Finally, it is considered as a regulating factor to enlarge or reduce the search step size.

[0033] Use the rime particle position vector to describe the search state: , In the formula For the i The first j The updated position of the rime particles, It is the best rime body in the current rime population. j The optimal position of rime particles, is a random parameter in the interval (-1,1); is the dynamic adjustment coefficient, The value of is dynamically adjusted by the iterative process. and Collaboratively determine the direction of particle movement; is the environmental adjustment coefficient, and its value is dynamically adjusted as the number of iterations accumulates to ensure the convergence of the algorithm; is a random variable between (0,1) and is used to adjust the distance between adjacent rime particles. for The moving upper boundary of for The moving lower boundary of and The effective range of the escape space; is a random number in the range (0,1). is the inter-particle adhesion effect parameter: , In the formula is the current iteration number, is the maximum number of iterations; Indicates rounding; is the dynamic inertia weight.

[0034] In this embodiment, the dynamic inertia weight The linear attenuation expression is: , In the formula is the initial inertia weight, is the final inertia weight.

[0035] This makes it possible to Larger value, larger This will make the update step longer, thus encouraging the algorithm to conduct a large-scale global search and help explore a wider search space. Gradually decreases, which means that the step size is reduced in the later updates, and the candidate solutions will be searched more finely near the current optimal solution, which helps to improve the local search ability and the accuracy of the solution. The algorithm can find a good balance between global search and local search by adjusting the value of .

[0036] (3) Hard rime puncture mechanism The RIME algorithm proposes a hard rime puncture mechanism based on the rime puncture phenomenon, which improves the algorithm's ability to escape local optima. The method of using the hard rime puncture mechanism to conduct a local fine search to determine the optimal rime particles corresponding to each rime body includes the following: The displacement between rime particles is expressed as follows: , In the formula is a random number between (-1,1), It is the normalized value of the fitness value of the current candidate solution, representing the probability of the current candidate solution being selected.

[0037] (4) Positive Greedy Selection Mechanism Finally, the method of determining the current optimal solution of the rime body is to evaluate the optimal candidate solutions obtained in adjacent iterations through the forward greedy selection mechanism: the forward greedy selection mechanism compares the fitness of the optimal candidate solutions obtained in adjacent iterations. When the new optimal candidate solution is better than the historical optimal candidate solution, dynamic replacement is triggered to obtain the current optimal solution.

[0038] The RIME algorithm improves upon the traditional greedy strategy by introducing a proactive forward optimization mechanism. Its core operational process is as follows: During each iteration, the system compares and evaluates the fitness of new and old candidate solutions. Dynamic replacement is triggered when the performance of a new solution outperforms that of the previous solution. At the optimization level, this mechanism continuously improves population quality through a selective retention strategy. However, its dynamic update nature can lead to performance fluctuations for some individuals. This seemingly contradictory phenomenon actually constitutes the algorithm's dual optimization characteristics, driving global convergence through the principle of elite retention while maintaining population heterogeneity through an unstable update mode. This dynamic balance mechanism effectively enhances the algorithm's topological space exploration capabilities, enabling it to exhibit stronger local extremum avoidance properties in complex optimization problems.

[0039] The essence of RIME's optimization lies in building an adaptive search balance system. By dynamically adjusting the weights between global exploration and local optimization, tailored to the specific requirements of different optimization problems, this system establishes a collaborative optimization mechanism for multi-scale search capabilities. This optimization approach maintains the algorithm's inherent advantages while enabling intelligent allocation of computing resources between broad-area exploration and detailed development.

[0040] 3) Experimental verification The experiment selected the Whale Optimization Algorithm (WOA), the Northern Goshawk Optimization Algorithm (NGO), the Rime Optimization Algorithm (RIME), and the Dynamic Inertia Weight Improved Rime Optimization Algorithm (DIW-RIME) proposed in this paper. The settings of the algorithm parameters are shown in Table 1.

[0041] Table 1 Key parameter settings of each algorithm The experiment selected 15 resource hardware, reliability and reinforcement cost as follows Figure 3 and Figure 4 shown.

[0042] like Figure 5 The optimization curve for the original Frost Ice algorithm shows that the algorithm successfully found the global optimal solution of 727.30 under the current experimental environment at the 27th iteration. However, after 30 iterations, the algorithm still exhibited significant parameter fluctuations, its convergence rate decreased, and it failed to effectively stabilize near the optimal solution.

[0043] The optimization process curve of the algorithm of the present invention based on dynamic weight improvement is as follows Figure 6 As shown in the figure, the algorithm successfully locates the optimal solution of the objective function, 727.30, in the sixth iteration. After 30 iterations, the parameter space still maintains good stability, and its convergence trajectory is smooth with no obvious fluctuations.

[0044] The convergence characteristics of the whale optimization algorithm are as follows Figure 7 As shown in Figure 2, the algorithm has a fast initial convergence rate and completes the convergence process in about 150 rounds, with the final stable solution being 748.60. Figure 5 、 Figure 6 Compared with the frost and ice algorithm series shown in Figure 3, there is an obvious gap in the quality indicators of the solution.

[0045] The results of the Northern Goshawk algorithm are as follows Figure 8 As shown in the figure, the algorithm decreases quickly and converges to the optimal solution of around 748.60 in about 125 rounds. Compared with the improved rime algorithm and the original rime algorithm, the optimization effect is poor.

[0046] Through multi-algorithm comparison experiments, such as Figures 5 to 8 As shown in the figure, the dynamic inertia weight RIME algorithm (DIW-RIME) proposed in this paper shows a significant performance advantage. Compared with the suboptimal solutions of the Northern Eagle algorithm (748.60) and the Whale algorithm (748.60), DIW-RIME achieves an optimization breakthrough of 2.78%-2.84% with an optimal solution value of 727.30. The mechanism analysis shows that the algorithm sets the inertia weight at the beginning of the iteration through a dynamic weight adjustment strategy. >1, the algorithm expands the exploration radius and accelerates the positioning of high-quality solution areas; in the later stage of iteration, it is adaptively adjusted to <1, effectively suppressing the fluctuation phenomenon in traditional algorithms.

[0047] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be employed in conjunction with other described embodiments.

Claims

1. A method for preventing and controlling faults in a ship's electronic information system based on backup and reconstruction, characterized in that: include: For the resource hardware of ship electronic information system, the system reliability is expressed by the weighted product of decision variables and single node static reliability. On the basis of establishing the system reliability and decision variable constraints, an objective function is established with the goal of minimizing the total reinforcement cost of resource hardware; The objective function is solved by using a rime optimization algorithm based on dynamic inertia weight. The decision variables corresponding to all resource hardware are regarded as rime bodies to form rime populations. All possible solutions of each rime body are regarded as rime particles. The expression of rime population based on rime particles is obtained. A soft rime search mechanism is used to perform a global search to update the rime population. A hard rime piercing mechanism is then used to perform a local fine search to determine the optimal rime particles corresponding to each rime body as the optimal candidate solution. The optimal candidate solutions obtained in adjacent iterations are then evaluated through a forward greedy selection mechanism to determine the current optimal solution for the rime body. The soft rime search mechanism is then returned to repeat the iterative update process until the final optimal solution for the decision variables is obtained. And perform hot backup of resource hardware based on the final optimal solution of the decision variables.

2. The method for preventing and controlling faults of ship electronic information systems based on backup and reconstruction according to claim 1 is characterized in that: Assume that the ship's electronic information system includes Resource hardware, resource hardware i As a node i Single-node static reliability , ; for nodes i Decision variables for reinforcement ; The static reliability of a single node after the node operation is expressed as ; When the node i Reinforcement, for: ； When the node i Without reinforcement, the static reliability of a single node remains ;Right now ; by Indicates the node i The cost of reinforcement; Representation node The weighting given to system reliability; The system reliability is expressed as : ； Further expanded to: 。 3. The method for preventing and controlling failures of ship electronic information systems based on backup and reconstruction according to claim 2 is characterized in that: The objective function is: ； The constraints are: ， 。 4. The method for preventing and controlling failures of ship electronic information systems based on backup and reconstruction according to claim 3 is characterized in that: The rime population is represented as , rime population include Rime body: ， In the formula Rime No. j rime particles; Then we get the expression of rime population based on rime particles: 。 5. The method for preventing and controlling failures of ship electronic information systems based on backup and reconstruction according to claim 4 is characterized in that: The method of using the soft rime search mechanism to perform global search and update the rime population is as follows: Use the rime particle position vector to describe the search state: ， In the formula For the i The first j The updated position of the rime particles, It is the best rime body in the current rime population. j The optimal position of rime particles, is a random parameter in the interval (-1,1); is the dynamic adjustment coefficient, is the environmental adjustment coefficient, is a random variable between (0,1), for The moving upper boundary of for The moving lower boundary of is a random number in the range (0,1), is the inter-particle adhesion effect parameter: ， In the formula is the current iteration number, is the maximum number of iterations; Indicates rounding; is the dynamic inertia weight.

6. The method for preventing and controlling failures of ship electronic information systems based on backup and reconstruction according to claim 5 is characterized in that: Dynamic inertia weight for: ， In the formula is the initial inertia weight, is the final inertia weight.

7. The method for preventing and controlling faults of ship electronic information systems based on backup and reconstruction according to claim 6 is characterized in that: The method of using the hard rime puncture mechanism to conduct a local fine search to determine the optimal rime particles corresponding to each rime body includes: ， In the formula is a random number between (-1,1), It is the normalized value of the fitness value of the current candidate solution.

8. The method for preventing and controlling failures of ship electronic information systems based on backup and reconstruction according to claim 7 is characterized in that: The method of determining the current optimal solution of the rime body by evaluating the optimal candidate solutions obtained in adjacent iterations through the forward greedy selection mechanism is as follows: the forward greedy selection mechanism compares the fitness of the optimal candidate solutions obtained in adjacent iterations. When the new optimal candidate solution is better than the historical optimal candidate solution, dynamic replacement is triggered to obtain the current optimal solution.

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