Task reliability modeling and analysis method based on dynamic reconstruction system

Abstract

The invention provides a task reliability modeling and analysis method based on a dynamic reconstruction system, which comprises the following steps of: performing element modeling on the dynamic reconstruction system by utilizing a resource operation capability support vector, and constructing a resource vector group and a task demand matrix to obtain an initial demand constraint model of the system; when the relationship between the resource vector group and the task demand matrix changes, change analysis is carried out through mathematical modeling, the existence and uniqueness of a capability-demand constraint solution are judged, and a new resource allocation scheme is generated; and calculating the resource residual capability and the function residual capability, and evaluating the actual residual capability of the system in combination with the reliability index to complete reliability calculation and analysis. According to the method, behaviors of the system in the dynamic reconstruction process can be effectively described, selection and optimization targets in the reconstruction process can be clarified, a quantitative basis is provided for a reconstruction decision of the system, resource residual capacity and function residual capacity can be calculated, and comprehensiveness and accuracy of reliability analysis are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability modeling and analysis, and in particular to a task reliability modeling and analysis method based on a dynamic reconfiguration system. Background Art

[0002] With the rapid development of information technology, traditional system architecture is difficult to cope with complex and dynamically changing environmental requirements. Dynamically reconfigurable system architecture came into being. For example, the ship's electronic information system is a dynamically reconfigurable system that needs to continuously provide highly reliable information support and decision support services in a dynamically changing environment. The main functions of the ship's electronic information system include navigation, communication, radar detection, electronic countermeasures, etc., and each function is supported by multiple sub-functional modules. For example, the navigation task can be decomposed into sub-tasks such as route planning, position correction, and chart update; the communication task may include sub-tasks such as data transmission, signal relay, and interference avoidance. The implementation of the functional module depends on the underlying resource operation capabilities, such as radar equipment, communication terminals, computing nodes, power supply and other key resources.

[0003] However, existing dynamic reconstruction systems still have deficiencies in multiple key links. On the one hand, in the reconstruction decision-making process, current methods often lack in-depth analysis of changes in resource capabilities and task requirements, making it difficult to accurately predict the trend and degree of change in a dynamic environment. This causes the generation of reconstruction solutions to rely too much on simple rules or fixed patterns, limiting the adaptability and flexibility of the system in complex scenarios. On the other hand, in the resource allocation process, existing technologies lack a global optimization analysis between task requirements and resource capabilities. Especially when faced with complex systems, they are unable to effectively balance the efficiency and cost of resource use, often resulting in redundant or insufficient resource allocation, affecting the overall effect of the system's dynamic reconstruction.

[0004] In addition, for reliability assessment after system reconstruction, most existing methods fail to fully consider the dynamic relationship between task requirements and remaining resource capacity, lack systematic indicators to measure resource and functional remaining capacity, and thus cannot fully reflect the actual reliability level of the system. Especially in the case of insufficient resources or sudden changes in demand, the system reconstruction plan lacks optimization strategies, which may further reduce the operational stability of the system. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a task reliability modeling and analysis method based on a dynamic reconfiguration system, which can effectively describe the behavior of the system during the dynamic reconfiguration process, clarify the selection and optimization goals in the reconfiguration process, provide a quantitative basis for the system's reconfiguration decision, and calculate the resource surplus capacity and functional surplus capacity, thereby ensuring the comprehensiveness and accuracy of the reliability analysis.

[0006] To achieve the above object, the present invention provides the following solution: a task reliability modeling and analysis method based on a dynamic reconfiguration system, comprising:

[0007] Using resource operation capability support vectors, tasks, subtasks, functions and resources in the dynamic reconfiguration system are modeled, resource vector groups and task requirement matrices are constructed, and the initial requirement constraint model of the system is obtained;

[0008] When the relationship between the resource vector group and the task requirement matrix changes, mathematical modeling is used to perform change analysis, determine the existence and uniqueness of the capability-demand constraint solution, generate a new resource allocation plan, and complete system reconstruction; wherein, when the capability-demand constraint solution has a unique solution, a new resource allocation plan is generated; when the capability-demand constraint solution has an infinite number of solutions, an objective function and constraint conditions are introduced into the system reconstruction mathematical model, and the optimal solution is calculated; when the capability-demand constraint solution has no solution, the system resources or requirements are readjusted to construct a new resource vector group and task requirement matrix;

[0009] After the dynamic reconstruction of the system is completed, the actual remaining capacity of the system is evaluated by calculating the remaining capacity of resources and functions and combining them with reliability indicators to complete the reliability calculation and analysis of the dynamically reconstructed system.

[0010] Optionally, resource operation capability support vectors are used to model tasks, subtasks, functions and resources in the dynamic reconfiguration system, including: using resource operation capability support vectors to establish modeling descriptions between tasks, subtasks, functions and resources in a step-by-step manner to obtain logical associations between tasks and resources; wherein a task is composed of multiple subtasks, a subtask is supported by multiple functions, and a function is supported by multiple resource operations.

[0011] Optionally, construct a resource vector group and a task requirement matrix to obtain an initial requirement constraint model of the system, including:

[0012] According to the logical association between tasks and resources, the task requirement matrix is ​​constructed using capability vectors, functional requirement support sets and resource support sets;

[0013] The task requirement matrix is ​​mapped and compared with the resource vector group, and the task requirement matrix is ​​decomposed layer by layer through the linear representation relationship between system elements, so that the system task requirements are mapped to the functional requirements and resource support level by level.

[0014] Optionally, when the relationship between the resource vector group and the task requirement matrix changes, mathematical modeling is used to perform change analysis, determine the existence and uniqueness of the capability-requirement constraint solution, generate a new resource allocation solution, and complete system reconstruction, including:

[0015] For the task requirement matrix, by solving the configuration vector and analyzing the capability-demand constraint solution, a current resource vector group for meeting the current task requirement is obtained, and according to the current resource vector group, a system reconstruction mathematical model is constructed to find a new resource configuration scheme;

[0016] When the capability-demand constraint solution has a unique solution, the current resource vector group meets the current task, and the configuration vector provides a resource configuration solution;

[0017] When the capability-demand constraint solution is infinite, the configuration vector is optimized and calculated, multiple linearly independent resource vector groups are found in the resource vector group, and an optimal resource vector group is selected to meet the current task requirement;

[0018] When the capability-demand constraint solution is unsolvable, the current resource vector group cannot meet the current task, the system resources or system requirements are readjusted, and a new resource vector group and task requirement matrix are constructed.

[0019] Optionally, when there are infinite solutions to the capability-demand constraint solution and there is no optimal resource vector group that meets the current task requirements, in the system reconstruction mathematical model, each linearly independent resource vector group generates a resource vector space, and in the resource vector space, the total resource cost of each resource vector group is calculated according to the usage cost of each resource; the calculation formula of the total resource cost is:

[0020]

[0021] Among them, f i (λ i ) is the cost function corresponding to the resource vector group, λ i is the linear combination coefficient of the resource vector group;

[0022] An objective function and constraints are introduced into the total resource cost, and the minimum total resource cost value is selected as the optimal solution for the dynamic reconstruction of the system.

[0023] Optionally, the objective function is formulated as:

[0024]

[0025] The constraint formula is:

[0026] B·X≥d c

[0027] X≤N

[0028] Among them, d c is the task requirement, N is the required resource scale vector, X is the demand-oriented hierarchical configuration, and B is.

[0029] Optionally, after the system dynamic reconstruction is completed, the actual remaining capacity of the system is obtained by calculating the remaining capacity of resources and the remaining capacity of functions, and the reliability calculation and analysis of the dynamically reconstructed system is completed, including:

[0030] A norm for measuring the resource synthesis capability vector is introduced into the resource capability vector model, and a weight vector for measuring the importance of each resource synthesis capability vector is set, and then the norm and the weight vector are used to define the system total capability level; the definition formula of the system total capability level is:

[0031]

[0032] Among them, C is the total resource cost, c is the norm, and w is the weight vector;

[0033] The support relationship between the resource and the task is calculated to obtain the remaining capacity and energy level of the resource and the remaining capacity and energy level of the function; the expression of the support relationship is:

[0034]

[0035] Among them, R is the resource vector group, X and Y are demand-oriented hierarchical configurations, is the total resource requirement vector of the function, is the total resource requirement vector of the task;

[0036] By comparing X with the scale vector of the resource, the remaining capacity and energy level of the resource can be obtained. By comparing Y with the scale vector of the function, the remaining capacity and energy level of the function can be obtained.

[0037] Optionally, after the system dynamic reconstruction is completed, the actual remaining capacity of the system is obtained by calculating the remaining capacity of resources and the remaining capacity of functions, and the reliability calculation and analysis of the dynamically reconstructed system is completed, which also includes:

[0038] The gap between the resource capacity and the expected value is calculated to measure the resource effectiveness. The expression of the resource effectiveness measurement is:

[0039]

[0040] Among them, ASR(i) is the resource effectiveness, δ r (e r ) is a mapping function of resource defects;

[0041] The output function set is used to measure the function effectiveness; the expression of the function effectiveness measurement is:

[0042]

[0043] F I ={F d1 ,F d2 ,…,F dm}

[0044] F di =(fd1,fd2,…,fd n )

[0045] F O ={F c1 ,F c2 ,…,F cm}

[0046] F ci =(fc1,fc2,…,fc n )

[0047] Among them, A F is the functional effectiveness, F O is the existing output function set of the system, F I is the input function set, F ci For function;

[0048] The actual remaining capacity of the system is calculated by multiplication; the expression of the actual remaining capacity is:

[0049]

[0050] Among them, A T is the actual surplus capacity, A R is resource effectiveness, A F For functional effectiveness.

[0051] The present invention discloses the following technical effects by providing a task reliability modeling and analysis method based on a dynamic reconfiguration system:

[0052] 1. The present invention effectively describes the dynamic reconstruction behavior of the system by constructing an equation of resource capacity and task requirement. This equation not only provides a quantitative basis for the system's reconstruction decision, but also allows real-time adjustment in actual operation to optimize the efficiency of resource allocation and task execution, and enables the system to maintain efficient operation in a changing environment with greater adaptability.

[0053] 2. By calculating the resource surplus capacity and functional surplus capacity, the invention provides a new method for comprehensively evaluating the reliability of system tasks. This evaluation not only takes into account the impact of a single resource or function, but also comprehensively reflects the interaction between the two, ensuring the comprehensiveness and accuracy of the reliability analysis. This feature makes this method suitable for various complex systems and can effectively support the making of key decisions.

[0054] 3. Through the dynamic reconfiguration behavior of the system, the present invention fully considers the relationship between resources, functions and tasks, and establishes a comprehensive modeling and analysis framework. In this way, the task reliability of the reconfigurable system in a dynamic environment can be more accurately evaluated, providing effective theoretical support and practical guidance for related applications.

[0055] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention;

[0058] Figure 2 A flowchart of dynamic reconstruction and reliability assessment provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of the dynamic reconstruction system requirement structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] like Figure 1 As shown, the present invention provides a task reliability modeling and analysis method based on a dynamic reconfiguration system, comprising:

[0063] 1. Use resource operation capability support vectors to model tasks, subtasks, functions and resources in the dynamic reconfiguration system, build resource vector groups and task requirement matrices, and obtain the initial requirement constraint model of the system. Including:

[0064] 1.1 Mathematical modeling of reconfigurable systems

[0065] like Figure 2-3 As shown in the figure, in the effectiveness organization of hierarchical dynamic reconfigurable systems, there are three main determining factors: resources, functions, and tasks. Formal description of these three factors is the premise of measuring the reliability of system tasks. There are a large number of public resources and a part of dedicated resources in the resource integration platform, which are connected and communicated through communication buses, such as integrated core processors, sensors, and communication equipment in electronic systems. The function integration platform uniformly organizes and manages the system's dedicated functions, logical functions, organizational functions, and application functions, which can be divided into key functions, important functions, basic functions, and auxiliary functions according to their impact on system tasks. The system management integration platform organizes and manages system tasks and professional tasks to provide logical guarantees, and the tasks can be divided into core tasks, basic tasks, guarantee tasks, and auxiliary tasks.

[0066] 1.11 Modeling of basic elements of the system

[0067] The basic elements of the system are tasks M y , Subtask T k Function k1 , Resources k There are four types of reconfigurable systems: y}, where M x ={T j ,T2,···,T k}, subtask M x ={T j ,T2,···,T k Function k1 、Function kt Support, the functional process consists of m resources {R j ,R2,···,R k Operation support can express the essence of task requirements through resource operation capability support vector. Through this step-by-step modeling description, the logical relationship between tasks and resources is clarified, providing a clear structured basis for subsequent mathematical modeling and analysis.

[0068] 1.12 Establish the initial demand constraint model of the system

[0069] A functional requirement can be represented by a capability vector, D f =(c i :n i ,…c j :n j ), the requirement of subtask T can be expressed as The requirement of task M can be expressed as Through the analysis of the task requirement tree, the capability support set of a task requirement can also be expressed by a capability vector, namely, d m =(n1·c1,n2·c2,…,n n ·c n ).

[0070] The system task requirements are Functional Requirements Support Set Resource support set but:

[0071] because:

[0072]

[0073] thus:

[0074]

[0075] D=B·A·R

[0076] in:

[0077]

[0078] Expanding the demand matrix D yields:

[0079]

[0080] Among them, d i is the demand vector at each level, then is the total demand vector.

[0081] Based on the above modeling process, the initial demand matrix of the system is constructed. By mapping and comparing the demand matrix with the resource matrix, the specific demand of the system for resource capacity at the current moment can be quantified. This analysis provides a rigorous theoretical basis for evaluating task reliability and lays the foundation for resource allocation optimization and system dynamic reconstruction.

[0082] The capability development process is the process of decomposing the system task requirements layer by layer through the linear representation relationship between system elements. That is, the process of expressing d through R, from the expectation through the vector group [r1 r2…r m ] T To express d, that is, d <= λ1r1+λ2r2+…+λ m r m ;λ1,λ2,…,λ m ∈N The system task requirement D should be composed of the system effective function set F = {F l ,F2,...,F x}Linear expression or partial linear expression. System function design requirements The system effective resource support set R = {R1:n1,R2:n2,...,R m :n m Through this hierarchical linear expansion method, the system task requirements are mapped to functional requirements and resource support step by step, realizing the capability decomposition from high-level task objectives to low-level resource support, and providing a clear mathematical basis for task reliability assessment and resource allocation optimization.

[0083] In summary, this step completes the modeling of system elements, establishes the resource vector group R and the task requirement matrix D, and gives the system constraints that can meet the normal execution of the task, that is, the task requirement D should be composed of the system effective function set F = {F l ,F2,...,F x}Linear expression or partial linear expression. System function design requirements The system effective resource support set R = {R1:n1,R2:n2,...,R m :n m However, the resource vector group R of the system will change as the system runs, which will cause the constraints to be unable to be met or partially unable to be met, and then cause the system task reliability to decrease from the initial state. When these quantities change, the resource vectors to which they belong will naturally change.

[0084] 2. If Figure 2-3 As shown, when the relationship between the resource vector group and the task requirement matrix changes, mathematical modeling is used to perform change analysis, determine the existence and uniqueness of the capability-demand constraint solution, generate a new resource allocation plan, and complete system reconstruction. When the capability-demand constraint solution has a unique solution, a new resource allocation plan is generated. When the capability-demand constraint solution has infinite solutions, the objective function and constraints are introduced into the system reconstruction mathematical model, and the optimal solution is calculated. When the capability-demand constraint solution has no solution, the system resources or requirements are readjusted to construct a new resource vector group and task requirement matrix. Including

[0085] 2.1 Mathematical modeling of system dynamic reconstruction behavior

[0086] The mathematical modeling steps of the system dynamic reconstruction behavior revolve around the dynamic balance between the system resource capacity and task requirements. When the system task reliability decreases over time, the relationship between the resource vector group R and the task requirement matrix D changes. Through mathematical modeling analysis, new resource allocation solutions are sought to maintain the overall service capacity of the system. After the system undergoes dynamic reconstruction behavior, the basic solution system for the following equation may still be satisfied.

[0087]

[0088] Y·X·R≥D

[0089] Y·X≤N

[0090] Among them, resource capabilities are organized as R1, R2, ..., R m Form a capability matrix R for a vector group, then R = (R1, R2, ..., R m ), D is a matrix composed of vectors of multiple task requirement levels, that is, D is contained in the column space of R, X, Y are the hierarchical configurations for requirement D, and N is the required resource scale vector. Except for the constraint condition Y×X≤N. There are three possible solutions to the formula: unique solution, infinite solutions, and no solution. The unique solution indicates that the current system resource capacity can just meet the demand; infinite solutions mean that the system can meet the demand through multiple configurations; no solution indicates that the system resources can no longer support the current task requirements.

[0091] The essence of reconstruction is to find a new current resource vector group that can meet the current needs from the changed P. When the system is reconstructed, it is necessary to give another maximal linearly independent group of P for D, which can still meet the task requirements. In this way, due to the compensation effect of logical reliability, although the physical reliability of the system has degraded, it does not affect the service capability of the system (that is, overall reliability).

[0092] When the system resource validity status allows, D can be represented by the resource vector group P (each component of X must meet the system scale constraint). If D cannot be represented by P, it means that the current resource platform cannot meet the demand constraint D. Due to the gradual degradation of the system's physical health, the initial balance between resource capabilities and task requirements is broken. In order to establish a new balance, it is necessary to find a new resource configuration based on the existing resource capabilities. According to the capability-demand model, when the system health status changes, the change in system capabilities causes the original capability matrix P to change to P1, and P1 is a matrix of the same shape as P. When D remains unchanged, the process of establishing a new capability-demand balance is system reconstruction, that is,

[0093] P1·Y≥D

[0094] Y≤N

[0095] From a mathematical point of view, solving the above equation can be expected to obtain the configuration vector Y. As mentioned above, the solutions of the model are divided into three categories: there is a unique solution, there are infinite solutions, and there is no solution. For a given requirement D, when there is a unique solution, it means that the current system capability composition just meets the system task requirement D. The solution vector Y gives the resource configuration vector. When there are infinite solutions, it means that the current system capability can meet the requirement D in a variety of configurations, and the solution vector Y is given by the general solution expression. When the above equation has a solution, if r = R(P) = R(P,D), it is critical to find a linearly independent group of number r, which is the supporting resource set of the system.

[0096] Get the required c The configuration is to find r linearly independent resource vector groups. When the number of resource types is greater than r, how to find a group of r independent resource vectors is the solution problem of the above equation, and how to get the best configuration is the optimization problem of reconfigurable system configuration. The situation where the equation is satisfied is the situation where the demand is exactly met.

[0097] In actual solution, for requirement D, we do not necessarily need a maximum linearly independent group, but only need to be able to express P1·Yd c ≥θ, but there must be R(G) ≤ r. For example, If d c =d1=(2,1), then a maximal linearly independent group of P1 is {R1, R2}, and a configuration is At the same time, 2·R3 T Can also satisfy d c In addition, if there is

[0098]

[0099] d c =d2=(2,1,0,0)

[0100] It can be obtained that the largest linearly independent group of P1 is (R1, R2, R3), for the equation P1·Y=d c For example, since R(P1)=3, R(P1,d c )=4, so there is no solution. But for d1, it can be satisfied by {2R1}, {2R3} or {R1, R2}.

[0101] In summary, after the mathematical model of system dynamic reconstruction is built through this step, the capacity-demand constraint solution is classified into three situations: there is a unique solution, infinite solutions, and no solution. When there is a unique solution, the system capacity just meets the task demand D, and the solution vector Y gives the resource configuration plan; when there are infinite solutions, the goal of system dynamic reconstruction is to select the solution with the highest system reliability from multiple configurations through optimization calculation; when there is no solution, it means that the current system resources cannot meet the task requirements, and resources or requirements need to be adjusted to restore balance. On this basis, the objective function and constraint conditions are further introduced to provide a calculation basis for achieving the optimal solution for system dynamic reconstruction.

[0102] 3. If Figure 2-3As shown, when the capability-demand constraint solution is infinite, if there is no solution (that is, there are infinite solutions and there is no optimal resource vector group that meets the current task requirements), the task requirements need to be downgraded and then solved again. When solving again, if there is a unique solution, there is no need to solve it, and the reconstruction is completed by direct output.

[0103] In the system reconstruction mathematical model, each linearly independent resource vector group generates a resource vector space, in which the total resource cost of each resource vector group is calculated according to the usage cost of each resource. Specifically, it includes:

[0104] The resource vector group P is an m×n matrix (R1, R2, …, Rm), d c For the current synthesis task requirements, if R(P) = R(P,d c )=m,when [P,d c ] is linearly dependent, and P is linearly independent, because P satisfies d c Therefore, P is called the minimum system configuration.

[0105] Given a set of n-dimensional vectors {R1, R2, …, R m}, the generated vector space is as follows:

[0106]

[0107] This means that a given set of resource vectors can be stretched into a resource vector space. However, for the current task requirements, the coordinates (λ1,λ2,…,λ m ) is limited. When there is more than one coordinate that meets the current task requirements, the system dynamically reconfigures the behavior in the hope of finding the best solution. From a mathematical point of view, if ... m ) The smaller it is, the fewer resources are used, and the better the solution is. From an engineering perspective, considering the cost of resources, the cost of using each resource is different, so the different configurations directly affect the total cost. Let the resource cost be represented by a vector w = (w1, w2, ..., w m ), then the total resource cost is:

[0108]

[0109] The resource vector group P is an m×n matrix (R1, R2, …, Rm). When the capacity-demand equation has a solution, if n < m, then the column vectors must be linearly correlated, and the system resources are in a configurable redundant state. R(P) = r, then the number of combinations of the maximal linearly independent group of P is C(r, m), which is the number of unconstrained system configurations. These configurations may all meet the task requirements d c, then for these C(r,m) configurations, we can use the lowest cost as the evaluation criterion. Considering universality, the cost function is given in vector form here.

[0110] F c =(f1(n1),f2(n2),…,f m (n m ))

[0111] Then there is

[0112]

[0113] Among them, f i (λ i ) is R i The corresponding cost function, λ i For R i The linear combination coefficients of .

[0114] The problem can be described as:

[0115] Objective function:

[0116]

[0117] Constraints:

[0118] B·X≥d c

[0119] X≤N

[0120] For each maximal linearly independent group, find the total cost C, and the one with the smallest C value is the optimal configuration. This is then reduced to an optimization problem, which is solved using integer programming methods.

[0121] 4. If Figure 2-3 As shown in FIG. 1 , after the dynamic reconstruction of the system is completed, the actual remaining capacity of the system is evaluated by calculating the remaining capacity of resources and functions and combining the reliability index to complete the reliability calculation and analysis of the dynamically reconstructed system.

[0122] In a reconfigurable system, resource capacity can be divided into three parts: 1) activated capacity, that is, in-use capacity or energy; 2) excess capacity, that is, due to resource calls, some of the capacity that does not participate in the calculation cannot be used by other tasks, which can also be considered as temporarily wasted capacity; 3) remaining capacity, that is, idle and allocable capacity. The total capacity is the sum of the available effective capacity of the system; the activated capacity is the in-use capacity, which can also be called energy. The energy level of a reconfigurable system is the percentage of the activated capacity to the total available effective capacity. By measuring the remaining capacity, a decision reference can be provided for system maintenance. The capacity activated by the task execution at a certain moment only indicates the capacity level required for the task, while the system reliability is related to other system capabilities. Therefore, the total capacity level of the system needs to be considered when investigating the system reliability.

[0123] The system's residual capacity can be divided into functional residual capacity and resource residual capacity. From the perspective of system maintenance, the residual capacity that is more meaningful for reference mainly refers to the resource residual capacity. During system operation, the resource residual capacity changes dynamically and varies with different function calls and task executions.

[0124] 4.1 Calculation of Remaining Resource Capacity

[0125] The resource vector group of the system is R = {R1, R2, ..., R m}, then the capability space is expressed as:

[0126] L={s=λ1R1+λ2R2+…+λ m R m |λ1,λ2,…,λ m ∈Z}

[0127] The capability space depends on the resource effectiveness of the system itself. The capability space can be a hypercube space covered by the capabilities of the Euclidean space determined by the resource vector group. Of course, due to the limitation of resource scale, the resource capability is only a spatial area.

[0128] From the resource capability vector model, we can get the resource synthesis capability vector c = (c1, c2, ..., c n ). In order to measure c, a quantity related to vector measurement must be introduced. Using the norm of c, it can be defined as Considering the different importance of ability items, the weight vector w=(w1,w2,...,w n ), considering the general situation, the total system capability level can be defined as:

[0129]

[0130] In a reconfigurable system, the support relationship between resources and tasks can be expressed as follows:

[0131]

[0132] Decomposing the formula is:

[0133]

[0134] in, represents the total resource requirement vector of the task, Represents the total resource demand vector of the function. Solving the equation X and Y is an integer programming problem. With the help of X and Y, we can get the actual support of resources for the task. Comparison can be made to obtain the remaining capacity and energy level of resources.

[0135] Resource effectiveness measurement focuses on estimating the gap between the existing capacity of resources and the expected resources, that is, the gap between the capacity of resource i and the expected It can be calculated by the following expression:

[0136]

[0137] Among them, design capability is used as a standard to set its Then there is Then the effectiveness of resource capabilities in the traditional sense can be measured by the following formula:

[0138]

[0139] Among them, δ r (e r ) represents the mapping function of resource defects, and its value in [0,1] reflects the relationship between resource r and other resources in the platform.

[0140] The remaining capacity of a single resource is related to the capacity structure of the resource, and the capacity weight should be considered when measuring it. For example, if a resource of resource R1 has capacity degradation (the defect weight is 0.15 and 0.2), the remaining capacity of the resource is estimated to be 0.65 according to the capacity weight.

[0141] 4.2 Calculation of functional residual capacity

[0142] By comparing Y with the function's scale vector, the function's remaining capacity and energy level can be obtained.

[0143]

[0144] Measuring the functional requirements of the system by the output function set can obtain the capability effectiveness of the functional integration platform. The priority of functional output may affect the effectiveness results of the functional platform, especially in the case of low functional redundancy.

[0145] Assume that the input function set is F I ={F d1 ,F d2 ,…,F dm}, where F di =(fd1,fd2,…,fd n ); the system's existing output function set is F O ={F c1 ,F c2 ,…,F cm}, where F ci =(fc1,fc2,…,fc n ). Then, for the function F ci calculate:

[0146]

[0147] Then, for the entire functional platform, the functional effectiveness A F It can be measured by the following formula:

[0148]

[0149] 4.3 Calculation of task reliability of reconfigurable system

[0150] The reliability of a reconfigurable system depends not only on the resources the system has (such as hardware, computing power, energy, etc.), but also on how these resources support the normal operation of the system functions. Resource surplus capacity reflects the potential of the system to continue to provide services in the current state, while functional surplus capacity measures how much of the original function the system can maintain in the face of failures or abnormalities. Therefore, resource surplus capacity and functional surplus capacity jointly determine the actual surplus capacity of the system, which in turn affects the reliability of the system.

[0151] A T =O(A R ,A F )

[0152] Where Ο represents a relationship or calculation method that describes how the system task reliability depends on the resource remaining capacity A R and functional residual capacity A F Calculating system reliability only by addition or linear combination may exaggerate the system's residual capacity. For example, when the residual capacity is high but the resources are almost exhausted, the system still cannot maintain normal operation.

[0153] In actual calculations, the following multiplication method is used to ensure that system reliability will remain high only when resources and functions reach a certain level. Conversely, if any one of them drops significantly, system reliability will drop rapidly.

[0154]

[0155] According to the above formula, the task reliability of the reconfigurable system can be calculated.

[0156] Therefore, the present invention provides a task reliability modeling and analysis method based on a dynamic reconstruction system, which can effectively describe the behavior of the system during the dynamic reconstruction process, clarify the selection and optimization goals in the reconstruction process, provide a quantitative basis for the system's reconstruction decisions, and calculate the resource surplus capacity and functional surplus capacity, thereby ensuring the comprehensiveness and accuracy of the reliability analysis.

[0157] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0158] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A task reliability modeling and analysis method based on a dynamically reconfigurable system, characterized in that: include: Using resource operation capability support vectors, tasks, subtasks, functions and resources in the dynamic reconfiguration system are modeled, resource vector groups and task requirement matrices are constructed, and the initial requirement constraint model of the system is obtained; When the relationship between the resource vector group and the task requirement matrix changes, mathematical modeling is used to perform change analysis, determine the existence and uniqueness of the capability-demand constraint solution, generate a new resource allocation plan, and complete system reconstruction; wherein, when the capability-demand constraint solution has a unique solution, a new resource allocation plan is generated; when the capability-demand constraint solution has an infinite number of solutions, an objective function and constraint conditions are introduced into the system reconstruction mathematical model, and the optimal solution is calculated; when the capability-demand constraint solution has no solution, the system resources or requirements are readjusted to construct a new resource vector group and task requirement matrix; After the dynamic reconstruction of the system is completed, the actual remaining capacity of the system is evaluated by calculating the remaining capacity of resources and functions and combining them with reliability indicators to complete the reliability calculation and analysis of the dynamically reconstructed system.

2. According to the method of task reliability modeling and analysis based on dynamic reconfiguration system in claim 1, it is characterized in that: The resource operation capability support vector is used to model the tasks, subtasks, functions and resources in the dynamic reconfiguration system, including: using the resource operation capability support vector, establishing the modeling description between tasks, subtasks, functions and resources in a step-by-step manner, and obtaining the logical association between tasks and resources; wherein a task is composed of multiple subtasks, a subtask is supported by multiple functions, and a function is supported by multiple resource operations.

3. According to claim 2, a task reliability modeling and analysis method based on a dynamic reconfiguration system is characterized in that: Construct resource vector groups and task requirement matrices to obtain the initial requirement constraint model of the system, including: According to the logical association between tasks and resources, the task requirement matrix is ​​constructed using capability vectors, functional requirement support sets and resource support sets; The task requirement matrix is ​​mapped and compared with the resource vector group, and the task requirement matrix is ​​decomposed layer by layer through the linear representation relationship between system elements, so that the system task requirements are mapped to the functional requirements and resource support level by level.

4. The task reliability modeling and analysis method based on a dynamic reconfiguration system according to claim 3 is characterized in that: When the relationship between the resource vector group and the task requirement matrix changes, mathematical modeling is used to perform change analysis, determine the existence and uniqueness of the capability-requirement constraint solution, generate a new resource allocation plan, and complete system reconstruction, including: For the task requirement matrix, by solving the configuration vector and analyzing the capability-demand constraint solution, a current resource vector group for meeting the current task requirement is obtained, and according to the current resource vector group, a system reconstruction mathematical model is constructed to find a new resource configuration scheme; When the capability-demand constraint solution has a unique solution, the current resource vector group meets the current task, and the configuration vector provides a resource configuration solution; When the capability-demand constraint solution is infinite, the configuration vector is optimized and calculated, multiple linearly independent resource vector groups are found in the resource vector group, and an optimal resource vector group is selected to meet the current task requirement; When the capability-demand constraint solution is unsolvable, the current resource vector group cannot meet the current task, the system resources or system requirements are readjusted, and a new resource vector group and task requirement matrix are constructed.

5. The task reliability modeling and analysis method based on a dynamic reconfiguration system according to claim 4 is characterized in that: When there are infinite solutions to the capability-demand constraint solution and there is no optimal resource vector group that meets the current task requirements, in the system reconstruction mathematical model, each linearly independent resource vector group generates a resource vector space, and in the resource vector space, the total resource cost of each resource vector group is calculated according to the usage cost of each resource; the calculation formula of the total resource cost is: Among them, f i (λ i ) is the cost function corresponding to the resource vector group, λ i is the linear combination coefficient of the resource vector group; An objective function and constraints are introduced into the total resource cost, and the minimum total resource cost value is selected as the optimal solution for the dynamic reconstruction of the system.

6. The task reliability modeling and analysis method based on a dynamic reconfiguration system according to claim 5 is characterized in that: The formula of the objective function is: The constraint formula is: B·X≥d c X≤N Among them, d c is the task requirement, N is the required resource scale vector, X is the demand-oriented hierarchical configuration, and B is.

7. The task reliability modeling and analysis method based on a dynamic reconfiguration system according to claim 6 is characterized in that: After the system is dynamically reconfigured, the actual remaining capacity of the system is evaluated by calculating the remaining capacity of resources and functions and combining the reliability index to complete the reliability calculation and analysis of the dynamically reconfigured system, including: A norm for measuring the resource synthesis capability vector is introduced into the resource capability vector model, and a weight vector for measuring the importance of each resource synthesis capability vector is set, and then the norm and the weight vector are used to define the system total capability level; the definition formula of the system total capability level is: Among them, C is the total resource cost, c is the norm, and w is the weight vector; The support relationship between the resource and the task is calculated to obtain the remaining capacity and energy level of the resource and the remaining capacity and energy level of the function; the expression of the support relationship is: Among them, R is the resource vector group, X and Y are demand-oriented hierarchical configurations, is the total resource requirement vector of the function, is the total resource requirement vector of the task; By comparing X with the scale vector of the resource, the remaining capacity and energy level of the resource can be obtained. By comparing Y with the scale vector of the function, the remaining capacity and energy level of the function can be obtained.

8. The task reliability modeling and analysis method based on a dynamic reconfiguration system according to claim 7 is characterized in that: After the system is dynamically reconfigured, the actual remaining capacity of the system is obtained by calculating the remaining capacity of resources and functions, completing the reliability calculation and analysis of the dynamically reconfigured system, which also includes: The gap between the resource capacity and the expected value is calculated to measure the resource effectiveness. The expression of the resource effectiveness measurement is: Among them, ASR(i) is the resource effectiveness, δ r (e r ) is a mapping function of resource defects; The output function set is used to measure the function effectiveness; the expression of the function effectiveness measurement is: F I ={F d1 ,F d2 ,…,F dm } F di =(fd1,fd2,…,fd n ) F O ={F c1 ,F c2 ,…,F cm } F ci =(fc1,fc2,…,fc n ) Among them, A F is the functional effectiveness, F O is the existing output function set of the system, F I is the input function set, F ci For function; The actual remaining capacity of the system is calculated by multiplication; the expression of the actual remaining capacity is: Among them, A T is the actual surplus capacity, A R is resource effectiveness, A F For functional effectiveness.

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