A task reliability optimization method for dynamic joint prevention and control of coupling damage trajectory

Abstract

The application provides a task reliability optimization method for coupling damage trajectory dynamic joint prevention and control, comprising the following steps: step one, modeling of task process damage deterioration trajectory; and step two, establishment and optimization of a task risk control strategy. Based on the joint dynamic prevention and control idea of associated damage, the application innovatively constructs a task reliability evaluation and optimization method for coupling of environmental impact-internal deterioration damage trajectory, aiming at the multi-type fault risk coupling problem in a complex task environment. On the basis of quantitative modeling and property analysis of the coupling damage trajectory, the application designs a detection-abort joint control strategy for a non-periodic task cycle, and develops a joint optimization method for the detection cycle and the abort threshold. The method can significantly improve the survival safety, task reliability and service economy of a task system in a complex competitive failure environment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of equipment reliability evaluation and optimization, and particularly relates to a continuous task reliability optimization method for multiple damage mode dependence under a random impact environment, which is suitable for system task reliability guarantee and risk control of multiple source fault risk coupling in a complex task environment. BACKGROUND

[0002] With the increasing scale and complexity of major task systems in key fields such as aerospace, rail transportation and advanced manufacturing, the external environment faced by these systems is increasingly complex, and especially the unpredictable risks brought by random environmental impact can significantly affect system performance, and even cause failure. In addition, the task system will also be continuously affected by the harsh environment during long-term operation, resulting in degradation of system performance. This cumulative degradation effect may eventually trigger failure or failure. Traditional operation and maintenance methods are difficult to effectively cope with these multiple failure modes, especially in reducing maintenance costs and improving task success rate, and existing solutions still have great deficiencies. Therefore, it is urgent to develop an innovative and efficient task reliability optimization strategy that can improve task reliability in complex service environments and reduce task failure losses.

[0003] In recent years, with the rapid progress of computer monitoring technology, condition-based maintenance research has gradually been implemented in task reliability guarantee. This technology collects and processes working conditions and health information during the task process in real time to provide information support for maintenance decision-making. However, current task reliability is mainly based on periodic detection and single control threshold static condition-based maintenance technology, which has significant deficiencies in real-time, accuracy and robustness when dealing with complex dynamic environments and associated failure modes. Especially for safety-critical task systems loaded on complex equipment such as aircraft, ships and rockets, any failure may bring great safety risks and losses, and traditional periodic maintenance and spare parts have been difficult to meet actual needs. Under this background, it is urgent to design an advanced operation and maintenance control strategy that can fully couple the service task characteristics and fault risk, effectively cope with the associated risks of external random impact and system damage degradation, and achieve the goal of guaranteeing task reliability and reducing task risk loss.

[0004] In order to effectively cope with the system safety risk caused by the internal degradation-external impact associated damage mode of the key task system in the complex environment, the application proposes a system task reliability dynamic evaluation and hierarchical task control decision optimization method for associated failure risk. The method analyzes the random environment impact-internal loss cumulative mode effect, constructs a task reliability model for evaluating the task failure risk under the associated risk mode, and designs a task risk termination strategy based on the threshold to guarantee the risk dynamic control ability of the task system in the task execution process, and realizes the global dynamic balance of the task reliability guarantee and the system survivability improvement.

[0005] The core of the application is to solve the control accuracy and real-time problem caused by the preset maintenance frequency period and level of the traditional operation and maintenance strategy through the cooperative control decision of the non-periodic task period detection-task termination. Through the joint dynamic optimization of the degradation state detection interval and the task termination threshold, the task reliability of the task system can be effectively improved, and the adaptability of the task system to the complex service environment can be effectively guaranteed, so as to reduce the risk loss in the task process. SUMMARY

[0006] The purpose of the application is to solve the fault risk quantification and evaluation problem of the continuous task system in the complex and severe task environment, guarantee the safe service and reliable operation under the associated damage mode and coupled damage trajectory, and the application innovatively develops a task reliability dynamic evaluation and optimization technical method for random environment impact-system degradation coupled damage. The method can effectively improve the accuracy and robustness of task risk control by constructing a multi-dimensional hierarchical control mechanism of task risk, designing a joint control strategy of state detection frequency-task termination threshold, and establishing a task reliability optimization model for the global coupled trajectory process, and provides a theoretical basis and technical support for the task success rate improvement and task safety guarantee under the severe service ring.

[0007] In order to achieve the above purpose, the application provides a task reliability optimization method for coupled damage trajectory dynamic joint prevention and control, which is realized through the following two steps:

[0008] Step 1: modeling of task process damage degradation trajectory;

[0009] The application uses a drift linear Wiener process model with an additional term to describe the performance evolution trajectory under the coupling mechanism of external random environment impact and internal degradation damage of the task system. The model quantitatively describes the performance degradation process of the system under the task environment stress level by introducing the random influence term of external impact and the cumulative effect of internal damage, thereby supporting the early warning of potential failure of the system. Specifically, the model describes the continuous degradation damage behavior of the task system through the following equation:

[0010]

[0011] where X(t) is the degradation amount of the mission system at time t; v is the drift coefficient; s is the diffusion coefficient; B(t) is the standard Brownian motion, i.e. B(t) ~ N(0,t 2 ). The arrival of external random shocks is described by a homogeneous Poisson process {N(t), t > 0}, N(t) is the cumulative number of shock arrivals at time t, and the frequency of shock arrival is l. The degradation increment of the system caused by each shock i obeys a normal distribution, i.e. 0 i ~ N(μ, ξ 2 ), μ is the mean of the degradation increment, and ξ 2 is the variance of the degradation increment. The degradation trajectory characteristics of the mission system under the coupling effect of external random shocks and system damage degradation are described by this model.

[0012] When the degradation amount of the mission system reaches the threshold L, the system will fail. Based on the dynamic modeling of probability distribution, the reliability function R(t) of the system at any time is derived:

[0013]

[0014] where Φ is the cumulative distribution function of the standard normal distribution. Through this formula, the comprehensive influence of average degradation, random disturbance and external shock on system degradation can be characterized, and the health status of the mission system at any time can be quantitatively described. On this basis, the probability density function of the degradation amount x of the system at time t is derived:

[0015]

[0016] where exp is the exponential function with the natural constant e as the base. This formula not only can effectively predict the probability distribution of system failure, but also can be used to support timely adjustment and maintenance of critical tasks.

[0017] According to the probability density function of the system degradation amount, for a random duration task, if the probability density function of its task duration τ is f τ (t), then the mission reliability function at time t can be expressed as:

[0018]

[0019] This formula contains the joint probability of the probability density function of the degradation amount x and the probability density function of the task duration τ. By combining the system degradation trajectory modeling and the randomness of the task duration, the mission reliability level under the random mission environment can be quantified.

[0020] Step two: establishment and optimization of mission risk control strategy.

[0021] Firstly, the cost function of a task cycle during the working period of a task system is established. For a task system, the time τ required to complete a task obeys an exponential distribution. The state of the system during the execution of the task is difficult to obtain in real time through continuous monitoring, so detection needs to be performed at fixed time intervals δ. The cost of each detection is C I The cost of a single detection C I and the number of detections n all affect the overall maintenance cost. A task abortion threshold D (D < L) is introduced to build a risk response and cost optimization strategy during the execution of the task, ensuring that the reliability of the task is improved while the maintenance and failure costs are minimized. At each detection point, the cost in the task cycle is divided into the following four scenarios: (1) task success, i.e., the task is successfully completed and the system degradation is less than D, indicating that the task is completed, and the task completion benefit C R is obtained; (2) task abortion, i.e., the system degradation exceeds D but is less than L, and the task is still not completed, the task abortion action is performed to avoid greater losses caused by failure, at this time only the cost of task failure C M is generated; (3) system failure, i.e., the system degradation exceeds L, system failure occurs, at this time the cost of task failure C M and the cost of system maintenance C F must be borne, so the cost borne at this time is C M + C F ; (4) no process loss: considering that the task duration τ obeys an exponential distribution, the task may still not be completed and the system degradation is less than the abortion threshold D at the nth detection. In this case, no process loss occurs. Specifically:

[0022] (1) task success

[0023] In this scenario, the system does not perform the task abortion action at the last degradation state detection, i.e., (n-1)δ, and successfully completes the task between the last and this degradation state detection, i.e., between (n-1)δ and nδ. The task system needs to meet the following conditions in this case: the task duration is between (n-1)δ and nδ, i.e., (n-1)δ < τ < nδ; the degradation of the system at time τ is less than the failure threshold L, i.e., X τ < L; the degradation of the system at (n-1)δ is less than the abortion threshold D, i.e., X (n-1)δ < D. Let u and o represent the degradation of the system at (n-1)δ and nδ, respectively, and the reliability of the task system at time t after (n-1)δ is:

[0024]

[0025] where h(k) denotes the probability of k external shocks occurring at time t. θ is the increment of degradation caused by external random shocks, and g(θ) is the distribution density function of θ, i.e.,

[0026]

[0027] Combining equations (3)-(6), the cost function of task success is:

[0028]

[0029] where, is the probability density function of task duration τ, C R is the task completion reward, f x [u,(n-1)δ] is the probability density function of system degradation x at (n-1)δ.

[0030] (2) Task Abortion

[0031] According to the above analysis, if the task abortion action is to be performed, the system needs to work normally at (n-1)δ and the degradation exceeds D at nδ. The conditions that the task system needs to meet in this case are: the task duration is greater than nδ, i.e., nδ<τ; the degradation of the system at (n-1)δ is less than the abortion threshold D, i.e., X (n-1)δ <D; and the degradation of the system at nδ is between the abortion threshold D and the failure threshold L, i.e., D nδ <L. The cost function of task abortion is:

[0032]

[0033] where P(u<D) denotes the probability that the degradation of the system at (n-1)δ is less than D, C I is the cost of single detection; C M is the cost of task failure; f x [o,nδ] is the probability density function of system degradation x at nδ.

[0034]

[0035] (3) System Failure

[0036] The task system fails under the combined action of external random shocks and system damage degradation. The conditions that need to be met in this scenario are: the system works normally at (n-1)δ and does not perform the abortion action, but the degradation exceeds L at nδ. The conditions that the task system needs to meet in this case are: the task duration is greater than nδ, i.e., nδ<τ; and the degradation of the system at nδ is greater than the failure threshold L, i.e., X nδL; the degradation amount of the system at the (n-1)th moment is less than the stop threshold value D, that is, X (n-1)δ The cost function of system failure is:

[0037]

[0038] Wherein, C F is the cost of system maintenance.

[0039] (4) No process loss

[0040] Since the task duration tau follows an exponential distribution, the situation that the task is not completed at the nth detection and the degradation amount of the system is less than the stop threshold value D may occur. The cost function of no process loss is recorded as:

[0041]

[0042] Combined with equations (7)-(11), the expected total cost in a task cycle is:

[0043]

[0044] The above steps are to analyze the loss function structure under the four situations of task success, task stop, system failure and no loss occurrence, construct the expected function of task cost, evaluate the risk degree and corresponding loss of each key decision point in the task completion process, and provide mode support for subsequent joint optimization of detection frequency and stop threshold value.

[0045] After the construction of the total cost function of the task system, a scheme for jointly and adaptively optimizing the maintenance detection interval delta and the task stop threshold value D is designed to reduce the expected loss caused by the non-reliability of the task. Since the optimization involves complex calculation of multiple integrals, it is difficult to directly obtain the analytical solution of the minimum cost, therefore, the invention introduces a traversal optimization algorithm to achieve the best balance between cost and reliability.

[0046] Traversal optimization algorithm:

[0047] 1. Input the degradation parameters, impact parameters and task cost parameters of the task system;

[0048] 2. Set the traversal range delta of the algorithm min <delta max ,D min <D<D max , search step length ADelta, AD, and traversal times N;

[0049] 3. Initialize the iteration times i=1, j=1, and the expected total cost EC=∞ in a task cycle;

[0050] 4. Let maintenance detection interval delta=deltamin +(i-1)Δδ;

[0051] 5. Let the task abort threshold D = D min +(j-1)ΔD;

[0052] 6. According to equations (5)-(12), the expected total cost EC * in a task cycle is calculated.

[0053] 7. Let EC = min{EC, EC *};

[0054] 8. Let j = j + 1, and judge whether j <= N is true, if true, return to step 5, if not true, go to step 9.

[0055] 9. Let i = i + 1, and judge whether i <= N is true, if true, let j = 1 and return to step 4, if not true, go to step 10.

[0056] 10. Output EC.

[0057] In summary, based on the joint dynamic prevention and control idea of associated damage, the task reliability evaluation and optimization method of environment impact-internal degradation damage trajectory coupling is innovatively constructed for the multi-fault risk coupling problem in complex task environment. Based on the quantitative modeling and property analysis of the coupled damage trajectory, a detection-abort joint control strategy for non-periodic task cycle is designed, and a joint optimization method of detection cycle and abort threshold is developed. The method can significantly improve the survival safety, task reliability and service economy of the task system in a complex competitive failure environment.

[0058] The advantages and beneficial effects of the present application are as follows:

[0059] 1. The continuous task reliability evaluation and optimization method proposed has strong universality and significant quantitative analysis effect on task process associated coupling risk

[0060] The continuous task reliability evaluation and optimization method proposed has good universality and can dynamically identify the multi-element competitive failure mode of the task system under the joint influence of external random impact and system damage degradation. The method can effectively represent the interaction form between different failure modes, realize deep analysis and quantification of task process associated risk, and provide targeted risk response scheme. Through joint modeling of external impact and internal damage degradation process, the model can accurately evaluate the reliability level of the task system in various complex environments, while providing real-time feedback of task success rate and system survival ability, and enhancing the explainability of the model to the task process associated risk.

[0061] 2. The hierarchical risk control strategy proposed has strong adaptability to complex environment and high multi-source fault control precision

[0062] The association damage joint dynamic prevention and control strategy can significantly improve the adaptability of the strategy to complex environments and the control accuracy of multi-source faults. Compared with traditional periodic maintenance strategies, the strategy can flexibly adjust the maintenance and control scheme through real-time task state and risk assessment dynamic updating, ensure high-precision fault control and real-time state feedback, and has higher robustness. By adaptively adjusting the maintenance scheme, the durability and environmental adaptability of the task system to harsh environments are improved, and the task failure loss is effectively reduced.

[0063] 3. It has wide application and promotion value in the field of system task reliability guarantee and risk control.

[0064] The prevention and control method is designed scientifically and reasonably, has strong operability and low implementation cost, and is especially suitable for practical engineering needs of large-scale task system reliability guarantee, covering multi-mode complex task systems such as shipborne equipment, unmanned aerial vehicle cluster and reusable rocket. In addition, the risk prevention and control strategy for non-periodic task process proposed by the method can fully adapt to the actual task time distribution type of complex task systems, and has wide application and promotion value. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The method flowchart described in the present application is shown.

[0066] Figure 2 The evaluation results of the total cost of the task under the maintenance detection interval and the task suspension threshold value bivariate optimization are shown. The figure directly shows the change trend of the total cost of the task under different detection interval and suspension threshold value combinations.

[0067] Figure 3 The optimization results of the detection interval under the condition of the fixed task suspension threshold value are shown. The figure shows the influence of different detection interval settings on the total cost of the task, and helps to determine the optimal task reliability optimization strategy.

[0068] Figure 4 The optimization results of the task suspension threshold value under the condition of the fixed detection interval are shown. The figure shows the influence of different task suspension threshold value settings on the total cost of the task, and helps to determine the optimal task reliability optimization strategy. DETAILED DESCRIPTION

[0069] The present application will be further described below in conjunction with examples.

[0070] In practical applications, shipboard phased array radar is an advanced radar system installed on naval vessels that utilizes phased array technology to achieve rapid beam scanning and target tracking through electronic control of phase adjustments. Shipboard phased array radar drivers are critical components specifically designed for phased array radar systems on naval vessels, primarily used to precisely control the phase and beam direction of the radar array to ensure efficient, flexible, and real-time scanning capabilities of the radar system. However, during detection missions exposed to harsh open-sea environments, drivers are particularly susceptible to defects caused by factors such as cracks and corrosion. Although these defects may not immediately cause the driver to fail, they gradually increase the probability of fatal failures over time. If the driver fails during operation, the mechanical structure of the radar will be damaged, ultimately leading to mission failure and irreversible loss of the entire radar system. In addition, the radar driver system may be subjected to random impacts from sea wave beating, strong winds, and occasional high-speed flying debris during long-term navigation. These impacts can cause additive damage to the precision structure of the radar driver and phased array module, particularly accelerating the expansion of existing micro-cracks and corrosion areas. Under the coupling effect of long-term internal damage degradation and external random impacts, the reliability of the radar driver system will be significantly threatened, and the probability of catastrophic failure will greatly increase. Therefore, the development of maintenance strategies for shipboard phased array radar driver systems must consider these impact-damage correlation factors to ensure the continuous and efficient operation of the radar under extreme conditions.

[0071] DETAILED DESCRIPTION The implementation is achieved through the following two steps:

[0072] Step 1: Data acquisition and modeling;

[0073] In this example, the damage degradation parameters of the radar driver system and the external random impact parameters are introduced into the model, and Table 1 lists the values of these parameters. Table 2 gives the maintenance cost parameters involved in the model. These parameters can be obtained from historical maintenance records and actual operation data. Through the combination of these data, the model can accurately assess the health status of the radar driver in complex environments and develop appropriate maintenance strategies, thereby improving the reliability of the system and reducing maintenance costs.

[0074] Table 1 Degradation and impact parameters of the radar driver system

[0075]

[0076] Table 2 Cost parameters in the model

[0077] Parameter C R ]]> C M ]]> [C F ]]> C I ]]> Meaning Task completion reward Task failure loss System failure loss Single detection cost Value 5 50 250 1

[0078] Step 2: Optimize mission system reliability and mission cost.

[0079] (1) Joint optimization of maintenance detection interval δ and task termination threshold D;

[0080] Substituting the parameters from Tables 1 and 2 into equations (5)-(12), and inputting them into the traversal optimization algorithm described above, while giving the optimization range of maintenance detection interval and task termination threshold as 0.1≤δ≤2, 1≤D<10, search step size Δδ=0.1, ΔD=0.1, the optimal combination with the lowest expected cost during system operation was found. That is, under the above parameter settings, when δ=0.3, D=4.6, the cost required by the task system to complete the task is reduced to the minimum, which is 34.7, as shown above. Figure 2 As shown in the figure. According to Equation (4), the reliability of the task at this time is 0.91, which shows that by jointly optimizing the above two parameters, it is possible to maintain high reliability of the task while effectively controlling maintenance costs.

[0081] (2) Optimization of detection interval δ under a fixed task termination threshold D;

[0082] After jointly optimizing the maintenance detection interval and task termination threshold, univariate optimization can be performed by simplifying the above traversal optimization algorithm. For example... Figure 3 As shown, the relationship between the total cost EC and the detection time interval δ is presented when the fixed task termination threshold D = 5. The results show that as the detection interval increases, the total cost first decreases and then gradually increases, exhibiting a significant nonlinear relationship. EC reaches its minimum value when δ = 0.3.

[0083] (3) Optimization of task termination threshold D under fixed detection interval δ.

[0084] Same as above, such as Figure 4 The figure shows the relationship between the total cost EC and the task abort threshold D when the detection interval δ = 0.3. The results show that the total cost EC first decreases and then slowly increases as the task abort threshold D increases. EC reaches its minimum value when D = 5. The reason for this trend is that as D increases, the probability of task success becomes higher and the probability of the task system bearing the cost of task failure decreases. However, when D continues to increase, an excessively high task abort threshold increases the probability of task system failure, leading to a greater probability of bearing the cost of task system failure.

[0085] The results demonstrate that by jointly optimizing the degradation state detection interval δ and the task abort threshold D, the proposed method significantly improves the reliability of the task system, reduces redundant detection frequency and maintenance costs, and is applicable to the reliability management of critical task systems in high-safety fields such as aerospace and rail transportation. This optimization method significantly improves the system's survivability and task success rate in complex and uncertain environments while reducing operational costs, demonstrating broad application potential in scenarios with high safety and reliability requirements.

[0086] In summary, the present application proposes a task reliability optimization method for coupling damage trajectory dynamic joint prevention and control. The method dynamically evaluates the risks faced by the system in the task process by constructing a task reliability evaluation model based on external impact and system degradation. By combining the adaptive termination strategy of task reliability with the detection interval optimization, the task termination action is taken in time when the risk rises to a certain threshold, effectively avoiding potential failure conditions and solving the limitations of traditional strategies that are difficult to deal with complex multiple failure modes. Through the interactive analysis of multiple competitive failure modes, the model can identify the health state changes of the task system in real time, so that the key tasks in complex environments can obtain more flexible and accurate maintenance and management support. The active task termination mechanism of the method ensures the survivability of the task system during execution, achieving the best effect in balancing task reliability and system survivability, and providing theoretical and technical support for the safety protection and reliability evaluation of task systems in complex environments.

Claims

1. A method for optimizing the reliability of a task by dynamically co-controlling coupled damage trajectories, characterized in that: This method is implemented through the following two steps: Step 1: Modeling the damage and degradation trajectory during the task process; A drift-linear Wiener process model with additional terms is used to describe the performance evolution trajectory of a mission system under the coupling mechanism of external random environmental shocks and internal degradation damage. By introducing the random influence term of external shocks and the cumulative effect of internal damage, the model quantifies the performance degradation process of the system under the stress level of the mission environment, thereby supporting early warning of potential system failure. Step Two: Establishing and optimizing task risk control strategies; First, establish the cost function within one task cycle during the operation of the task system; for a task system, the time required to complete the task. It follows an exponential distribution; the system's state during task execution is difficult to obtain in real time through continuous monitoring, therefore it needs to be monitored at fixed time intervals. Conduct testing; cost of a single test and number of tests All of these will affect the overall maintenance cost; introducing a task termination threshold To construct risk management and cost optimization strategies during task execution, ensuring improved task reliability while minimizing maintenance and failure costs; among other things... ; This is the failure threshold; In step one, the damage and degradation trajectory of the task process is modeled, and represented as: (1) in, For the task system in The amount of degradation at any given time; This is the drift coefficient; The diffusion coefficient is denoted as . It is standard Brownian motion, that is The arrival of external random shocks occurs via a homogeneous Poisson process. To describe, for The cumulative number of times an impact arrives, and the frequency of impact arrivals. The incremental degradation of the system with each impact It follows a normal distribution, that is... , This represents the mean of the degradation increment. The variance of the degradation increment; In step two, at each detection point, the cost within the task cycle is divided into the following four scenarios: (1) Task success, that is, the task is successfully completed and the system degradation is less than This indicates that the task is complete and the task completion reward is received. (2) Task termination, i.e., system degradation exceeds But smaller than If the task is not yet completed, the operation is suspended to avoid greater losses due to failure. In this case, only the cost of task failure is incurred. (3) System failure, i.e., the amount of system degradation exceeds If a system failure occurs, the cost of the task failure must be borne by the user. and system maintenance costs The cost borne at this time is (4) No process loss occurs: considering the duration of the task Following an exponential distribution, the first possible occurrence is... The task was not completed during the next test and the system degradation was less than the abort threshold. In this case, no process loss occurs.

2. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 1, characterized in that: When the degradation of the task system reaches the failure threshold When the system fails, it will fail; based on dynamic modeling of probability distribution, the reliability function of the system at any time is derived. : (2) in, Let be the cumulative distribution function of the standard normal distribution; this formula can characterize the combined effects of average degradation, random disturbances, and external shocks on system degradation, and quantitatively describe the health state of the task system at any given time; based on this, the system's... Degradation at any given time The probability density function: (3) in, For the natural constant An exponential function with base 0.

3. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 2, characterized in that: Based on the probability density function of system degradation, for a task of random duration, if its task duration... The probability density function is Then in The task reliability function at time t is expressed as: (4)。 4. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 1, characterized in that: Task successful: The system was in the last degradation state detection, i.e. The mission was suspended before the last degradation status detection was completed, i.e. arrive The task is successfully completed within a certain timeframe; in this case, the task system needs to meet the following condition: the task duration is within a certain timeframe. and Between, that is The system is in The degradation at any given time is less than the failure threshold. ,Right now The system is in The degradation at time point is less than the termination threshold. ,Right now ;make and These respectively represent the system in and The amount of degradation over time, the task system in After The reliability of the time is: (5) in, express External shocks occur at any time The probability of this time; This represents the incremental degradation caused by external random shocks to the system. yes The probability density function of the distribution is: (6) (7) The cost function for a successful task is: .(8) in, For the duration of the task The probability density function, Rewards for completing the task For the system in Degradation over time Values The probability density function.

5. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 1, characterized in that: Task Abort: To execute a task abort action, the system must be in a state of... It works normally and Time degradation exceeds The task system in this case needs to meet the following condition: the task duration is greater than... ,Right now The system is in The degradation at time point is less than the termination threshold. ,Right now The system is in The amount of degradation at time cessation threshold and failure threshold Between, that is The cost function for task termination is: (9) in, Indicates that the system is in The amount of degradation at time is less than The probability, Cost per test; The cost of mission failure; For the system in Degradation over time Values The probability density function; (10)。 6. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 1, characterized in that: System failure: The task system fails under the combined effects of external random shocks and system damage and degradation; this scenario requires the system to meet the following condition: Normal operations were conducted at the time, and no suspension action was implemented; however, in The amount of degradation exceeded The task system in this case needs to meet the following condition: the task duration is greater than... ,Right now The system is in The degradation at any given time is greater than the failure threshold. ,Right now The system is in The degradation at time point is less than the termination threshold. ,Right now The cost function for system failure is: (11) in, The cost of system maintenance.

7. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 1, characterized in that: No process loss occurs: due to the duration of the task Following an exponential distribution, the first possible occurrence is... The task was not completed during the next test and the system degradation was less than the abort threshold. In the case where no process loss occurs, the cost function is denoted as: (12) The expected total cost over a task cycle is: (13)。 8. The task reliability optimization method for dynamic joint prevention and control of coupled damage trajectories according to claim 1, characterized in that: A traversal optimization algorithm is introduced to achieve the best balance between cost and reliability; the traversal optimization algorithm is as follows: A. Input the degradation parameters, impact parameters, and task cost parameters of the task system; B. Set the traversal range of the algorithm Search step size Number of traversals ; C. Initialize the number of iterations The expected total cost within a task cycle ; D. Set maintenance and inspection intervals ; E. Set the task termination threshold. ; F. Calculate the expected total cost over a task cycle. ; G. Order ; H. Order ,judge If the condition is met, return to step E; otherwise, proceed to step I. I. Order ,judge Whether it is valid or not, if it is valid, then... Then return to step D; if the condition is not met, proceed to step J. J. Output .

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